MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v002

Bohumir Jelinek; Sebastien Groh; Mark F. Horstemeyer; Jeff Houze; Seong-Gon Kim; Gregory J. Wagner; Amitava Moitra; Michael I. Baskes

doi:10.25950/8d75422b

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MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_002

Interatomic potential for Aluminum (Al), Copper (Cu), Iron (Fe), Magnesium (Mg), Silicon (Si).
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Title A single sentence description.	MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v002
Description A short description of the Model describing its key features including for example: type of model (pair potential, 3-body potential, EAM, etc.), modeled species (Ac, Ag, ..., Zr), intended purpose, origin, and so on.	A set of modified embedded-atom method (MEAM) potentials for the interactions between Al, Si, Mg, Cu, and Fe was developed from a combination of each element's MEAM potential in order to study metal alloying. Previously published MEAM parameters of single elements have been improved for a better agreement to the generalized stacking fault energy (GSFE) curves when compared with ab initio generated GSFE curves. The MEAM parameters for element pairs were constructed based on the structural and elastic properties of element pairs in the NaCl reference structure garnered from ab initio calculations, with adjustment to reproduce the ab initio heat of formation of the most stable binary compounds. The new MEAM potentials were validated by comparing the formation energies of defects, equilibrium volumes, elastic moduli, and heat of formation for several binary compounds with ab initio simulations and experiments. Single elements in their ground-state crystal structure were subjected to heating to test the potentials at elevated temperatures. An Al potential was modified to avoid the formation of an unphysical solid structure at high temperatures. The thermal expansion coefficient of a compound with the composition of AA 6061 alloy was evaluated and compared with experimental values. MEAM potential tests performed in this work, utilizing the universal atomistic simulation environment (ASE), are distributed to facilitate reproducibility of the results.
Species The supported atomic species.	Al, Cu, Fe, Mg, Si
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	LAMMPS package 22-Sep-2017
Content Other Locations	https://openkim.org/id/Sim_LAMMPS_MEAM_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__SM_656517352485_000

Contributor	Yaser Afshar
Maintainer	Yaser Afshar
Developer	Bohumir Jelinek Sebastien Groh Mark F. Horstemeyer Jeff Houze Seong-Gon Kim Gregory J. Wagner Amitava Moitra Michael I. Baskes
Published on KIM	2023
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Jelinek B, Groh S, Horstemeyer MF, Houze J, Kim SG, Wagner GJ, et al. Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys. Phys Rev B. 2012Jun;85:245102. doi:10.1103/PhysRevB.85.245102 — (Primary Source) A primary source is a reference directly related to the item documenting its development, as opposed to other sources that are provided as background information. [2] Jelinek B, Groh S, Horstemeyer MF, Houze J, Kim S-G, Wagner GJ, et al. MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v002. OpenKIM; 2023. doi:10.25950/8d75422b [3] Afshar Y, Hütter S, Rudd RE, Stukowski A, Tipton WW, Trinkle DR, et al. The modified embedded atom method (MEAM) potential v002. OpenKIM; 2023. doi:10.25950/ee5eba52 [4] Tadmor EB, Elliott RS, Sethna JP, Miller RE, Becker CA. The potential of atomistic simulations and the Knowledgebase of Interatomic Models. JOM. 2011;63(7):17. doi:10.1007/s11837-011-0102-6 [5] Elliott RS, Tadmor EB. Knowledgebase of Interatomic Models (KIM) Application Programming Interface (API). OpenKIM; 2011. doi:10.25950/ff8f563a Click here to download the above citation in BibTeX format.
Citations This panel presents information regarding the papers that have cited the interatomic potential (IP) whose page you are on. The OpenKIM machine learning based Deep Citation framework is used to determine whether the citing article actually used the IP in computations (denoted by "USED") or only provides it as a background citation (denoted by "NOT USED"). For more details on Deep Citation and how to work with this panel, click the documentation link at the top of the panel. The word cloud to the right is generated from the abstracts of IP principle source(s) (given below in "How to Cite") and the citing articles that were determined to have used the IP in order to provide users with a quick sense of the types of physical phenomena to which this IP is applied. The bar chart shows the number of articles that cited the IP per year. Each bar is divided into green (articles that USED the IP) and blue (articles that did NOT USE the IP). Users are encouraged to correct Deep Citation errors in determination by clicking the speech icon next to a citing article and providing updated information. This will be integrated into the next Deep Citation learning cycle, which occurs on a regular basis. OpenKIM acknowledges the support of the Allen Institute for AI through the Semantic Scholar project for providing citation information and full text of articles when available, which are used to train the Deep Citation ML algorithm.	This panel provides information on past usage of this interatomic potential (IP) powered by the OpenKIM Deep Citation framework. The word cloud indicates typical applications of the potential. The bar chart shows citations per year of this IP (bars are divided into articles that used the IP (green) and those that did not (blue)). The complete list of articles that cited this IP is provided below along with the Deep Citation determination on usage. See the Deep Citation documentation for more information. 206 Citations (142 used) Show Model Used Show All Help us to determine which of the papers that cite this potential actually used it to perform calculations. If you know, click the . “Growth and annealing effect on the Cu thin film deposited on Si (001) surface,” Journal of Crystal Growth. 2022. link Times cited: 0 B. Waters, D. S. Karls, I. Nikiforov, R. Elliott, E. Tadmor, and B. Runnels, “Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework,” Computational Materials Science. 2022. link Times cited: 5 K. Wu et al., “A comparative study of interfacial thermal conductance between metal and semiconductor,” Scientific Reports. 2022. link Times cited: 1 H. Chabba and D. Dafir, “Atomistic Simulation Study of Mechanical Deformation of Al-Mg-Si Alloys,” International Journal of Engineering Research in Africa. 2021. link Times cited: 0 Abstract: Aluminum alloys have been attracting significant attention. … read more Abstract: Aluminum alloys have been attracting significant attention. Especially Al-Mg-Si alloys can exhibit an excellent balance between strength and ductility. Deformation mechanisms and microstructural evolution are still challenging issues. Accordingly, to describe how the type of phase influence mechanical behaviour of Al/Mg/Si alloys, in this paper atomic simulations are performed to investigate the uniaxial compressive behaviour of Al-Mg-Si ternary phases. The compression is at the same strain rate (3.1010 s−1); using Modified Embedded Atom Method (MEAM) potential to model the deformation behaviour. From these simulations, we get the total radial distribution function; the stress-strain responses to describe the elastic and plastic behaviors of GP-AlMg4Si6, U2-Al4Mg4Si4 and β-Al3Mg2Si6 phases. For a Detailed description of which phase influence hardness and ductility of these alloys; the mechanical properties are determined and presented. These stress-strain curves obtained show a rapid increase in stress up to a maximum followed by a gradual drop when the specimen fails by ductile fracture. From the results, it was found that GP-AlMg4Si6 & U2-Al4Mg4Si4 phases are brittle under uniaxial compressive loading while β-Al3Mg2Si6 phase is very ductile under the same compressive loading. The engineering stress-strain relationship suggests that β-Al3Mg2Si6 phase have high elasticity limit, ability to resist deformation and have the advantage of being highly malleable. Molecular dynamics software LAMMPS was used to simulate and build the Al-Mg-Si ternary system. read less R. R. Santhapuram, D. Spearot, and A. Nair, “Role of grain boundaries and substrate in plastic deformation of core–shell nanostructures,” Journal of Materials Science. 2020. link Times cited: 0 G. H. Lee, C. Cui, and H. Beom, “Energy release rate of hyperelastic solids with a nanocrack,” Philosophical Magazine Letters. 2020. link Times cited: 0 Abstract: ABSTRACT The purpose of this study is to explore the hyperel… read more Abstract: ABSTRACT The purpose of this study is to explore the hyperelastic effect on the energy release rate of a crack extension at the nanoscale. A molecular statics computation has been carried out to characterise the atomistic nature of fracturing. The concept of the J-integral is employed to measure the energy release rate for Ni and Si single crystals having a hyperelastic nature. The obtained J-integral is compared to the energy release rates predicted using the two-specimen method and the finite-element method. The results show that the effect of the highly-localized nonlinear zone in front of the crack tip potentially induces a breakdown of the linear elastic fracture mechanics model. read less W. Jia et al., “Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning,” SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. 2020. link Times cited: 146 Abstract: For 35 years, ab initio molecular dynamics (AIMD) has been t… read more Abstract: For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD applications are limited by computational cost to systems with thousands of atoms at most. We report that a machine learningbased simulation protocol (Deep Potential Molecular Dynamics), while retaining ab initio accuracy, can simulate more than 1 nanosecond-long trajectory of over 100 million atoms per day, using a highly optimized code (GPU DeePMD-kit) on the Summit supercomputer. Our code can efficiently scale up to the entire Summit supercomputer, attaining 91 PFLOPS in double precision (45.5% of the peak) and 162/275 PFLOPS in mixed-single/half precision. The great accomplishment of this work is that it opens the door to simulating unprecedented size and time scales with ab initio accuracy. It also poses new challenges to the next-generation supercomputer for a better integration of machine learning and physical modeling. read less H. Jang and J. W. Hong, “Influence of Zinc Content on the Mechanical Behaviors of Cu-Zn Alloys by Molecular Dynamics,” Materials. 2020. link Times cited: 5 Abstract: The mechanical properties of copper alloys containing variou… read more Abstract: The mechanical properties of copper alloys containing various ratios of zinc are evaluated using molecular dynamics (MD) simulations to determine the impact of the different zinc concentrations. The modified embedded atom method (MEAM) parameters for copper were established in the 1990s; however, the MEAM potential parameters for zinc, with an axial ratio >1, were recently proposed. In this research, the MD models of the copper alloys with various zinc contents are constructed using the MEAM potential parameters for zinc. Tensile test simulations are also conducted. The strain rate effects of the alloys are evaluated at four different strain rates, and the variations in the tensile strengths and Young’s modulus are investigated. The proposed procedures have significant potential applicability for simulating a variety of zinc-containing alloys. read less R. A. Fleming, J. A. Goss, and M. Zou, “Material dimensionality effects on the nanoindentation behavior of Al/a-Si core-shell nanostructures,” Applied Surface Science. 2017. link Times cited: 12 R. A. Fleming and M. Zou, “The effects of confined core volume on the mechanical behavior of Al/a-Si core-shell nanostructures,” Acta Materialia. 2017. link Times cited: 18 H. Fan and J. El-Awady, “Towards resolving the anonymity of Pyramidal slip in magnesium,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2015. link Times cited: 66 M. Liao, B. Li, and M. Horstemeyer, “Interaction Between Basal Slip and a Mg17Al12 Precipitate in Magnesium,” Metallurgical and Materials Transactions A. 2014. link Times cited: 29 K. A. Bukreeva, A. Iskandarov, S. Dmitriev, Y. Umeno, and R. Mulyukov, “Theoretical shear strength of FCC and HCP metals,” Physics of the Solid State. 2014. link Times cited: 12 E. Asadi, M. A. Zaeem, A. Moitra, and M. Tschopp, “Effect of vacancy defects on generalized stacking fault energy of fcc metals,” Journal of Physics: Condensed Matter. 2014. link Times cited: 28 Abstract: Molecular dynamics (MD) and density functional theory (DFT) … read more Abstract: Molecular dynamics (MD) and density functional theory (DFT) studies were performed to investigate the influence of vacancy defects on generalized stacking fault (GSF) energy of fcc metals. MEAM and EAM potentials were used for MD simulations, and DFT calculations were performed to test the accuracy of different common parameter sets for MEAM and EAM potentials in predicting GSF with different fractions of vacancy defects. Vacancy defects were placed at the stacking fault plane or at nearby atomic layers. The effect of vacancy defects at the stacking fault plane and the plane directly underneath of it was dominant compared to the effect of vacancies at other adjacent planes. The effects of vacancy fraction, the distance between vacancies, and lateral relaxation of atoms on the GSF curves with vacancy defects were investigated. A very similar variation of normalized SFEs with respect to vacancy fractions were observed for Ni and Cu. MEAM potentials qualitatively captured the effect of vacancies on GSF. read less H. Shodja, M. Tabatabaei, A. Ostadhossein, and L. Pahlevani, “Elastic fields of interacting point defects within an ultra-thin fcc film bonded to a rigid substrate,” Central European Journal of Engineering. 2013. link Times cited: 4 Abstract: Certain physical and mechanical phenomena within ultra-thin … read more Abstract: Certain physical and mechanical phenomena within ultra-thin face-centered cubic (fcc) films containing common types of interacting point defects are addressed. An atomic-scale lattice statics in conjunction with many-body interatomic potentials suitable for binary systems is conducted to analyze the effects of the depth on the: (1) formation energy and layer-by-layer displacements due to the presence of vacancy-octahedral self-interstitial atom (OSIA) ensemble, and (2) elastic fields as well as the free surface shape in the case of vacancy-dopant interaction. Moreover, the effects of the inter-defect spacing for various depths are also examined. To ensure reasonable accuracy and numerical convergence, the atomic interaction up to the second-nearest neighbor is considered. read less S. Yan, “Multiscale study of the properties of hybrid laser-welded Al-Mg-Si alloy joints.” 2019. link Times cited: 3 Abstract: ............................................................… read more Abstract: ................................................................................................................................... VI read less D. Sun, “Proliferation of Twinning in Metals: Application to Magnesium Alloys.” 2018. link Times cited: 2 Abstract: In the search for new alloys with a great strength-to-weight… read more Abstract: In the search for new alloys with a great strength-to-weight ratio, magnesium has emerged at the forefront. With a strength rivaling that of steel and aluminum alloys --- materials which are deployed widely in real world applications today --- but only a fraction of the density, magnesium holds great promise in a variety of next-generation applications. Unfortunately, the widespread adoption of magnesium is hindered by the fact that it fails in a brittle fashion, which is undesirable when it comes to plastic deformation mechanisms. Consequently, one must design magnesium alloys to navigate around this shortcoming and fail in a more ductile fashion. However, such designs are not possible without a thorough understanding of the underlying mechanisms of deformation in magnesium, which is somewhat contested at the moment. In addition to slip, which is one of the dominant mechanisms in metallic alloys, a mechanism known as twinning is also present, especially in hexagonal close-packed (HCP) materials such as magnesium. Twinning involves the reorientation of the material lattice about a planar discontinuity and has been shown as one of the preferred mechanisms by which magnesium accommodates out-of-plane deformation. Unfortunately, twinning is not particularly well-understood in magnesium, and needs to be addressed before progress can be made in materials design. In particular, though two specific modes of twinning have been acknowledged, various works in the literature have identified a host of additional modes, many of which have been cast aside as "anomalous" observations. To this end, we introduce a new framework for predicting the modes by which a material can twin, for any given material. Focusing on magnesium, we begin our investigation by introducing a kinematic framework that predicts novel twin configurations, cataloging these twins modes by their planar normal and twinning shear. We then subject the predicted twin modes to a series of atomistic simulations, primarily in molecular statics but with supplementary calculations using density functional theory, giving us insight on both the energy of the twin interface and barriers to formation. We then perform a stress analysis and identify the twin modes which are most likely to be activated, thus finding the ones most likely to affect the yield surface of magnesium. Over the course of our investigation, we show that many different modes actually participate on the yield surface of magnesium; the two classical modes which are accepted by the community are confirmed, but many additional modes --- some of which are close to modes which have been previously regarded as anomalies --- are also observed. We also perform some extensional work, showing the flexibility of our framework in predicting twins in other materials and in other environments and highlighting the complicated nature of twinning, especially in HCP materials. read less E. Asadi, M. A. Zaeem, and M. Baskes, “Phase-Field Crystal Model for Fe Connected to MEAM Molecular Dynamics Simulations,” JOM. 2014. link Times cited: 31 Y. Li et al., “Novel green chemical mechanical polishing for an aluminum alloy and mechanisms interpreted by molecular dynamics simulations and measurements,” Surfaces and Interfaces. 2023. link Times cited: 0 M. Dias, P. Carvalho, A. Gonçalves, E. Alves, and J. B. Correia, “Hybrid molecular dynamic Monte Carlo simulation and experimental production of a multi-component Cu–Fe–Ni–Mo–W alloy,” Intermetallics. 2023. link Times cited: 1 D. G. Kizzire et al., “Modified embedded atom method interatomic potential for FCC γ-cerium,” Computational Materials Science. 2023. link Times cited: 0 T. Yang, X. Han, W. Li, X. Chen, and P. Liu, “Angular dependent potential for Al-Zr binary system to study the initial heterogeneous nucleation behavior of liquid Al on L12-Al3Zr,” Computational Materials Science. 2023. link Times cited: 0 M. Muralles, J. T. Oh, and Z. Chen, “Modified embedded atom method interatomic potentials for the Fe-Al, Fe-Cu, Fe-Nb, Fe-W, and Co-Nb binary alloys,” Computational Materials Science. 2023. link Times cited: 0 G. Lei, H.-tao Gao, Y. Zhang, X.-hui Cui, and H.-liang Yu, “Atomic-level insights on enhanced strength and ductility of Al−Mg−Si alloys with β″-Mg5Si6 at cryogenic temperatures,” Transactions of Nonferrous Metals Society of China. 2023. link Times cited: 0 G. Lei, J.-rui Xing, H.-tao Gao, X.-hui Cui, and H.-liang Yu, “Effect of temperature on near-surface microstructural evolution in nanocrystalline metal under shear stress,” Journal of Central South University. 2023. link Times cited: 0 M. Lablali, H. Mes-adi, A. Eddiai, and M. Mazroui, “Effect of stepped Si (001) substrate on Cu thin film growth,” Surface Topography: Metrology and Properties. 2023. link Times cited: 1 Abstract: The growth processes of Cu thin film on stepped Si(001) subs… read more Abstract: The growth processes of Cu thin film on stepped Si(001) substrate were investigated using molecular dynamics simulation. The modified embedded atom method was used to describe the atomic interaction between Cu-Cu, Si-Si, and Si-Cu. In this study, four different Si(001) substrate configurations were examined: (i) flat Si(001) substrate; (ii) stepped Si surface with 3-monoatomic layers step; (iii) Stepped Si surface with 5-monoatomic layers step; (iiii) stepped surface with 7-monoatomic layers. Our aim here is to investigate the effect of stepped substrate on the structure, the surface roughness, and the morphology of deposited Cu thin film. The results show that the Cu film obtained has a crystalline structure based on the radial distribution function. In addition, the morphology of Cu film is not smooth for the different stepped substrates. More precisely, the surface roughness increases when the substrate presents a step and rises with the augmentation of the step height. On the other hand, our results reveal that the penetration of Cu atoms in the simplest case of the flat configuration is limited to the top layer of the substrate. While for the stepped substrate, our findings show that the penetration in the stepped substrate is more important and deeper within the upper terrace compared to the lower terrace. Furthermore, the numerical calculations demonstrate that the step height has no significant effect on the penetration of Cu atoms on the Si(001) stepped substrate. These results are appropriate for the deposition of copper atoms into the stepped substrate of silicon. read less M. Taoufiki, H. Chabba, A. Barroug, A. Jouaiti, and D. Dafir, “The Bond Length of Intermetallic Ternary Phases of Al-Fe-Si Alloy Using Molecular Dynamics Simulation with the Application of [001] Compression,” International Journal of Engineering Research in Africa. 2023. link Times cited: 0 Abstract: The research on tolerance stress in aluminum alloys is focus… read more Abstract: The research on tolerance stress in aluminum alloys is focused on examining the mechanical behavior of τ4-Al3FeSi2 and τ12-Al3Fe2Si phases during [001] compression and their structural evolution. The use of MD computational bond length measurements allows for a comparison to be made with previous studies on tensile deformation. The simulations were performed at a constant strain rate of 21×1010 s-1, using NPT conditions (isothermal-isobaric), with approximately 20,000 atoms, 1 atmosphere of pressure, and 300 K temperature, using a Nosé-Hoover thermostat. Under periodic boundary conditions, the Modified Embedded Atoms Method (MEAM) potential was applied to all 3D faces, and the average bond length behavior between Al, Fe, and Si was calculated. A comprehensive investigation is carried out to explore the properties of these phases, including a detailed structural analysis at the atomic scale. This paper presents a comprehensive analysis of how changes in compound concentration affect mechanical behavior during compression. The average bond length varies depending on the applied stress axis, and it demonstrates good agreement with literature data. The mechanical deformations alter the behavior of atomic phases, as discussed in detail in the conclusion. read less Á. D. Carral, X. Xu, S. Gravelle, A. YazdanYar, S. Schmauder, and M. Fyta, “Stability of Binary Precipitates in Cu-Ni-Si-Cr Alloys Investigated Through Active Learning,” SSRN Electronic Journal. 2023. link Times cited: 0 I. Mukherjee and P. Das, “A Molecular Dynamics Study of Nucleation and Grain Growth of Novel Al-15Mg2Si-4.5Si Composite during Rapid Cooling Based Semi Solid Slurry Preparation,” Solid State Phenomena. 2023. link Times cited: 0 Abstract: Owing to their several attractive features such as high hard… read more Abstract: Owing to their several attractive features such as high hardness, high elastic modulus, light weight, high strength to weight ratio, high thermal conductivity, and high temperature strength, composites from Al-Mg2Si family offers promise towards deployment in several industries such as automobile, aerospace, marine, defence and electronic. The present molecular dynamics (employing LAMMPS) based simulation study is one of the first attempt to investigate the nucleation and grain growth mechanisms of Mg2Si phase at atomic level in case of novel Al-15Mg2Si-4.5Si composite, during semi-solid processing. Modified embedded atom method (MEAM) potential has been used to study the atomic interactions in the composite. Reaching the melt state at 1000 K, the temperature of the system is first decreased from 1000 K to 853 K and then the system is held at 853 K for 100 ps. The simulations are performed with three different cooling rates. With lowering of temperature, randomly distributed Mg and Si atoms form atomic clusters at arbitrary locations within the system, which is the nucleation stage for Mg2Si phase formation. Cluster size, radial distribution function has been used to investigate the structural evolution of Mg-Si clusters. Cooling rate significantly influences the grain size as well as the grain growth kinetics. The information about the thermodynamic state of the system has been revealed by extracting the values of internal energy, enthalpy, specific heat. during the slurry preparation and isothermal holding stages. The growth mechanism of Mg2Si nucleus has been characterized from the temporal variation of (Mg + Si) atoms taking part in the cluster formation. Power-law variation is observed in the cooling stage whereas a linear variation is observed in the isothermal stage. read less A. Moitra, “Twin-boundary and precipitate interaction in Mg–Al alloy: an MD study,” Modelling and Simulation in Materials Science and Engineering. 2023. link Times cited: 0 Abstract: Strengthening of Mg-alloys by precipitation is much less eff… read more Abstract: Strengthening of Mg-alloys by precipitation is much less efficient than in other metallic alloys (e.g. Al) as the Mg17Al12 precipitates grow as thin plate or lozenge shaped or long rod shape parallel to the basal plane. Recently atomistic simulations reveal that the dislocation-precipitate interaction is very week to claim for the precipitation hardening mechanism. However, the interaction of twin-boundary with the Mg17Al12 precipitate remains unexplored using atomistic simulation. In the present study we focus on the twin-boundary/precipitate interaction at different temperatures, precipitate sizes and varied applied loads, carried out using classical molecular dynamics methodology. In particular, the activation energies necessary to overcome various precipitates are determined as a function of the temperature, precipitate size and applied load. The velocity profile of the twin is calibrated with these different external conditions. An attractive nature of interaction has been observed while the twin-boundary comes closer to the precipitate and a network of dislocations are observed when the twin-boundary bypass the precipitate, as manifested through our atomistic microstructures. These results provide valuable information about the precipitate hardening mechanisms and suggested new avenues to improve the mechanical properties of Mg–Al alloys. read less S. Ataollahi and M. Mahtabi, “An interatomic potential for ternary NiTiHf shape memory alloys based on modified embedded atom method,” Computational Materials Science. 2023. link Times cited: 2 C. Yilmaz, M. Poul, L. Lahn, D. Raabe, and S. Zaefferer, “Dislocation-Assisted Particle Dissolution: A New Hypothesis for Abnormal Growth of Goss Grains in Grain-Oriented Electrical Steels,” SSRN Electronic Journal. 2023. link Times cited: 2 I. Shtablavyi, N. Popilovskyi, Y. Kulyk, R. Serkiz, B. Tsizh, and S. Mudry, “Formation of nanoscale phases during rapid solidification of Al–Cu–Si alloys,” Applied Nanoscience. 2023. link Times cited: 0 T. An et al., “Effect of Si segregation at grain boundaries on the mechanical behaviours of ageing Al metallization layer in insulated gate bipolar transistor module,” Molecular Simulation. 2023. link Times cited: 0 Abstract: ABSTRACT In this study, the evolution of the Si atom distrib… read more Abstract: ABSTRACT In this study, the evolution of the Si atom distribution within an Al metallization layer in an insulated gate bipolar transistor (IGBT) module during power cycling is studied experimentally through electron backscatter diffraction (EBSD) observations, X-ray diffraction (XRD) measurements, and scanning electron microscopy (SEM) characterization. Molecular dynamics (MD) simulations are applied to study the effects of Si segregation on the mechanical properties of the Al metallization layer. The results show that Si segregation toward the Al grain boundaries occurs as the number of power cycles increases, and this behaviour strongly influences the recrystallization, texture, grain size and mechanical performance of the Al metallization layer. read less L. Han, H. Y. Song, M. An, T. Shen, and Y. Li, “A novel strengthening mechanism in crystalline/amorphous dual-phase Mg alloys: A molecular dynamics study,” Journal of Non-Crystalline Solids. 2023. link Times cited: 2 S. Risal et al., “Development of the RF-MEAM Interatomic Potential for the Fe-C System to Study the Temperature-Dependent Elastic Properties,” Materials. 2023. link Times cited: 0 Abstract: One of the major impediments to the computational investigat… read more Abstract: One of the major impediments to the computational investigation and design of complex alloys such as steel is the lack of effective and versatile interatomic potentials to perform large-scale calculations. In this study, we developed an RF-MEAM potential for the iron-carbon (Fe-C) system to predict the elastic properties at elevated temperatures. Several potentials were produced by fitting potential parameters to the various datasets containing forces, energies, and stress tensor data generated using density functional theory (DFT) calculations. The potentials were then evaluated using a two-step filter process. In the first step, the optimized RSME error function of the potential fitting code, MEAMfit, was used as the selection criterion. In the second step, molecular dynamics (MD) calculations were employed to calculate ground-state elastic properties of structures present in the training set of the data fitting process. The calculated single crystal and poly-crystalline elastic constants for various Fe-C structures were compared with the DFT and experimental results. The resulting best potential accurately predicted the ground state elastic properties of B1, cementite, and orthorhombic-Fe7C3 (O-Fe7C3), and also calculated the phonon spectra in good agreement with the DFT-calculated ones for cementite and O-Fe7C3. Furthermore, the potential was used to successfully predict the elastic properties of interstitial Fe-C alloys (FeC-0.2% and FeC-0.4%) and O-Fe7C3 at elevated temperatures. The results were in good agreement with the published literature. The successful prediction of elevated temperature properties of structures not included in data fitting validated the potential’s ability to model elevated-temperature elastic properties. read less S. Paul, D. Schwen, M. Short, and K. Momeni, “A Modified Embedded-Atom Method Potential for a Quaternary Fe-Cr-Si-Mo Solid Solution Alloy,” Materials. 2023. link Times cited: 0 Abstract: Ferritic-martensitic steels, such as T91, are candidate mate… read more Abstract: Ferritic-martensitic steels, such as T91, are candidate materials for high-temperature applications, including superheaters, heat exchangers, and advanced nuclear reactors. Considering these alloys’ wide applications, an atomistic understanding of the underlying mechanisms responsible for their excellent mechano-chemical properties is crucial. Here, we developed a modified embedded-atom method (MEAM) potential for the Fe-Cr-Si-Mo quaternary alloy system—i.e., four major elements of T91—using a multi-objective optimization approach to fit thermomechanical properties reported using density functional theory (DFT) calculations and experimental measurements. Elastic constants calculated using the proposed potential for binary interactions agreed well with ab initio calculations. Furthermore, the computed thermal expansion and self-diffusion coefficients employing this potential are in good agreement with other studies. This potential will offer insightful atomistic knowledge to design alloys for use in harsh environments. read less G. Xie, F. Wang, X. Lai, Z. Xu, and X.-guo Zeng, “Atomistic study on crystal orientation-dependent crack propagation and resultant microstructure anisotropy in NiTi alloys,” International Journal of Mechanical Sciences. 2023. link Times cited: 1 M. Pole et al., “Modes of Strain Accommodation in Cu-Nb multilayered thin film on Indentation and Cyclic Shear,” Surfaces and Interfaces. 2023. link Times cited: 1 M. Ramesh, D. Kumar, and A. Kumar, “Investigation of the effect of age hardening on the mechanical properties of aluminium metal matrix composites reinforced with graphite particulate,” Materials Today: Proceedings. 2023. link Times cited: 1 M. Batyrow, I. Erucar, and H. Öztürk, “Size dependent change of mean square displacement in gold nanocrystals: A molecular dynamics simulation,” Concurrency and Computation: Practice and Experience. 2023. link Times cited: 2 Abstract: Thermally activated atomic vibrations significantly decrease… read more Abstract: Thermally activated atomic vibrations significantly decrease the x‐ray diffraction intensities of nanocrystalline powders. Hence their quantification is critical for accurate structural characterization of small nanocrystals by x‐ray diffraction. In this study, atomic vibrations in the form of mean square displacements (MSDs) in 5, 10, 15, 20, and 30 nm diameter spherical gold nanocrystals were computed by molecular dynamics (MD) simulations at room temperature and below. A strong size and temperature dependency of MSD was observed from spherical gold nanocrystals. Moreover, these displacements increased radially from the center of the nanocrystals and reached a maximum at the surface layers due to the presence of undercoordinated surface atoms and their relatively unrestricted motions. High temperature simulations were performed to investigate the evolution of structural stability of nanoparticles with increasing temperature. Surface melting was observed before complete melting of nanocrystals. Results of this work will be useful to understand the effect of nanocrystal size on the amplitude of thermally activated atomic vibrations and their quantification in measured intensities by x‐ray diffraction experiments. read less H. Issa, A. Taherizadeh, and A. Maleki, “Atomistic study of the effect of grain size and reinforcement particle on mechanical behavior of magnesium / silica nanocomposite,” Materialia. 2022. link Times cited: 2 H. Yuan et al., “Flexible alumina films prepared using high-bias pulse power for OLED thin film encapsulation,” Ceramics International. 2022. link Times cited: 1 M. Taoufiki, H. Chabba, D. Dafir, A. Barroug, M. Boulghallat, and A. Jouaiti, “Atomistic Investigation Using Molecular Dynamics Simulation of τ4-Al3FeSi2 and τ12-Al3Fe2Si Phases under Tensile Deformation,” International Journal of Engineering Research in Africa. 2022. link Times cited: 1 Abstract: Aluminum-Iron-Silicon (Al-Fe-Si) alloys are extremely applie… read more Abstract: Aluminum-Iron-Silicon (Al-Fe-Si) alloys are extremely applied in many specific industries, such as aerospace and automobiles. Their atomic concentration influences the mechanical behavior of the investigated τ4-Al3Fe2Si and τ12-Al3FeSi2 phases. The uniaxial-tensile deformation is used to compare their structural evolution under the same conditions.Atomic displacement and mechanical behavior have an interest in the elastic and plastic areas. Stress-Strain responses and Radial Distribution Function (RDF) are required. Further, atomic simulations using molecular dynamics demonstrate the change occurs. Its process is carried out at a strain rate of 21×1010 s-1 using the NPT (isothermal-isobaric) with roughly 20 700 atoms at a pressure of 105 Pa. Furthermore, using a Nosée Hoover thermostat at the temperature of 300 k is decisive.The Modified Embedded Atoms Method (MEAM) is the applied potential between Al, Fe, and Si atoms. The elastic modulus and single pair atomic correlation before and after straining are increased by this method. The atomic correlations are shown in short- and long-range order and the τ12-Al3Fe2Si phase illustrates stronger properties compared to τ4-Al3Fe2Si phase. Our results underscore an important variation associated with the change of iron and silicon concentration. More specifics are covered in the selection paper. read less J. Du, H. Y. Song, M. An, and Y. Li, “Effect of rare earth element on amorphization and deformation behavior of crystalline/amorphous dual-phase Mg alloys,” Materials & Design. 2022. link Times cited: 5 Y. Kashyrina, A. S. Muratov, V. Kazimirov, and O. S. Roik, “X-ray diffraction study and molecular dynamic simulation of liquid Al-Cu alloys: a new data and interatomic potentials comparison,” Journal of Molecular Modeling. 2022. link Times cited: 0 S. Gowthaman, T. Jagadeesha, and V. Dhinakaran, “Analysis on the Impact of Creep Environment and Grain Size During Biaxial Creep Characterization on the Creep Features of Ferrosilicon Alloy: a Molecular Dynamics Study,” Silicon. 2022. link Times cited: 1 V. Plechystyy, I. Shtablavyi, B. Tsizh, S. Mudry, and J. Rybicki, “Atomic Composition and Structure Evolution of the Solid-Liquid Boundary in Al-Si System During Interfacial Diffusion and Contact Melting,” Journal of Phase Equilibria and Diffusion. 2022. link Times cited: 0 M. Wang, X. Huang, S. Wu, and G. Dai, “Molecular dynamics simulations of tensile mechanical properties and microstructures of Al-4.5Cu alloy: the role of temperature and strain rate,” Modelling and Simulation in Materials Science and Engineering. 2022. link Times cited: 1 Abstract: In this paper, the deformation behavior of Al-4.5Cu alloys c… read more Abstract: In this paper, the deformation behavior of Al-4.5Cu alloys containing the Cu clusters under high temperature is systematically investigated by molecular dynamics. Reduced nucleation stress of dislocation is driven by increasing strain rate and temperature, which triggers the stress–strain curves of Al-4.5Cu alloy showing gradually elastic-plastic stage and elastic-plastic-viscous stage. Besides, the defect surface in Al-4.5Cu alloy do not have enough time to move along and gather due to the increase of strain rate, which make the distribution of defect surface mainly divide into three types: the plane distribution which is at 45° angle to the direction of tensile, the stratification distribution which is perpendicular to the direction of tensile and the distribution of honeycomb, respectively. The microscopic fracture morphology for Al-4.5Cu alloy are changed from pure shear fracture to microporous aggregate fracture due to three type of defect surface. read less N. Chen, S. Hu, W. Setyawan, B. Gwalani, P. Sushko, and S. Mathaudhu, “Formation and dissociation of shear-induced high-energy dislocations: insight from molecular dynamics simulations,” Modelling and Simulation in Materials Science and Engineering. 2021. link Times cited: 3 Abstract: Solid-phase processing (SPP) allows one to create complex mi… read more Abstract: Solid-phase processing (SPP) allows one to create complex microstructures, not achievable via thermal processing alone. The resulting structures exhibit a rich palette of defects, both thermal and non-thermal, including defect substructures, such as dislocation networks. It is essential to understand the mechanisms of deformation and defect structure formation to guide SPP towards achieving desired microstructures and material properties. In this study, large-scale molecular dynamics simulations are used to investigate the effects of inhomogeneous strain distribution, that mimics deformation conditions of tribological tests, on the evolution of defects under severe shear deformation in polycrystalline Al. Analysis of defect nucleation and reaction pathways reveals that strong geometric constraints suppress the nucleation and slide of low energy dislocation 1/2⟨110⟩{111} but promote the nucleation and slide of high energy dislocations, such as 11¯0 (001) and 1/2 11¯2¯ (1 1¯ 1). A rough contact surface, characteristic to tribological tests, imposes an inhomogeneous stress field leading to inhomogeneous defect substructures due to location-dependent activation of slip systems. The results suggest that high-energy dislocations can dominate the evolution of grain structures in highly constrained environments, which should be considered in modeling plastic deformation and grain refinement during SPP. read less Z. Jian et al., “Shock-induced plasticity and phase transformation in single crystal magnesium: an interatomic potential and non-equilibrium molecular dynamics simulations,” Journal of Physics: Condensed Matter. 2021. link Times cited: 8 Abstract: An effective and reliable Finnis–Sinclair (FS) type potentia… read more Abstract: An effective and reliable Finnis–Sinclair (FS) type potential is developed for large-scale molecular dynamics (MD) simulations of plasticity and phase transition of magnesium (Mg) single crystals under high-pressure shock loading. The shock-wave profiles exhibit a split elastic–inelastic wave in the [0001]HCP shock orientation and a three-wave structure in the [10-10]HCP and [-12-10]HCP directions, namely, an elastic precursor, a followed plastic front, and a phase-transition front. The shock Hugoniot of the particle velocity (U p) vs the shock velocity (U s) of Mg single crystals in three shock directions under low shock strength reveals apparent anisotropy, which vanishes with increasing shock strength. For the [0001]HCP shock direction, the amorphization caused by strong atomic strain plays an important role in the phase transition and allows for the phase transition from an isotropic stressed state to the product phase. The reorientation in the shock directions [10-10]HCP and [-12-10]HCP, as the primary plasticity deformation, leads to the compressed hexagonal close-packed (HCP) phase and reduces the phase-transition threshold pressure. The phase-transition pathway in the shock direction [0001]HCP includes a preferential contraction strain along the [0001]HCP direction, a tension along [-12-10]HCP direction, an effective contraction and shear along the [10-10]HCP direction. For the [10-10]HCP and [-12-10]HCP shock directions, the phase-transition pathway consists of two steps: a reorientation and the subsequent transition from the reorientation hexagonal close-packed phase (RHCP) to the body-centered cubic (BCC). The orientation relationships between HCP and BCC are (0001)HCP ⟨-12-10⟩HCP // {110}BCC ⟨001⟩BCC. Due to different slipping directions during the phase transition, three variants of the product phase are observed in the shocked samples, accompanied by three kinds of typical coherent twin-grain boundaries between the variants. The results indicate that the highly concentrated shear stress leads to the crystal lattice instability in the elastic precursor, and the plasticity or the phase transition relaxed the shear stress. read less M. Khalid, J. Friis, P. H. Ninive, K. Marthinsen, I. G. Ringdalen, and A. Strandlie, “Modified embedded atom method potential for Fe-Al intermetallics mechanical strength: A comparative analysis of atomistic simulations,” Physica B-condensed Matter. 2021. link Times cited: 4 M. Wagih and C. Schuh, “Thermodynamics and design of nanocrystalline alloys using grain boundary segregation spectra,” Acta Materialia. 2021. link Times cited: 20 V. Plechystyy et al., “Effect of heat treatment on the diffusion intermixing and structure of the Cu thin ﬁlm on Si (111) substrate: a molecular dynamics simulation study,” Molecular Simulation. 2021. link Times cited: 3 Abstract: ABSTRACT This work is devoted to the study of the diffusion … read more Abstract: ABSTRACT This work is devoted to the study of the diffusion process at the interface between copper films with a thickness of 2, 3, 4, 7 and 10 atomic monolayers and silicon substrate by molecular dynamics simulation method. For this purpose, the variation of the concentration of copper and silicon along the perpendicular direction to the interface was investigated. An analysis of the density profile along this direction made it possible to determine the melting point of the interface between copper and silicon. The atomic structure of the diffusion layer was compared with that of the bulk Cu3Si compound. Using the formalism pair correlation functions find partial coordination numbers distribution it was revealed the possibility of nucleation centres formation with the structure of the compound in the liquid state. This work will allow expanding knowledge about the process of atomic diffusion at the metal–semiconductor interface. read less H. Song et al., “Inelastic phonon transport across atomically sharp metal/semiconductor interfaces,” Nature Communications. 2021. link Times cited: 10 Z. Lu, N. P. Smith, M. Prange, R. A. Bunker, J. Orrell, and A. Chaka, “Effect of interfacial structures on phonon transport across atomically precise Si/Al heterojunctions,” Physical Review Materials. 2021. link Times cited: 3 Abstract: Phonons are important carriers of energy and information in … read more Abstract: Phonons are important carriers of energy and information in many cryogenic devices used for quantum information science and in fundamental physics experiments such as dark matter detectors. In these systems phonon behaviors can be dominated by interfaces and their atomic structures; hence, there is increasing demand for a more detailed understanding of interfacial phonon transport in relevant material systems. Previous studies have focused on understanding thermal transport over the entire phonon spectrum at and above room temperature. At ultralow temperatures, however, knowledge is missing regarding athermal phonon behavior due to the challenge in modeling the extreme conditions in microscale, heterogeneous cryogenic systems, as well as extracting single-phonon information from a large ensemble. In this paper, we delineate the effects of interfacial atomic structures on phonon transport using a combination of classical molecular dynamics (MD) and phonon wave-packet simulations, to illustrate the consistency and differences between the ensemble- and single-phonon dynamics. We consider three single-crystal Si surface reconstructions---$(1\ifmmode\times\else\texttimes\fi{}1), (\sqrt{3}\ifmmode\times\else\texttimes\fi{}\sqrt{3})$ and $(7\ifmmode\times\else\texttimes\fi{}7)$---and model both experimentally observed $\mathrm{Si}(1\ifmmode\times\else\texttimes\fi{}1)/\mathrm{Al}$ interfaces and hypothesized $\mathrm{Si}(\sqrt{3}\ifmmode\times\else\texttimes\fi{}\sqrt{3})$/Al and $\mathrm{Si}(7\ifmmode\times\else\texttimes\fi{}7)/\mathrm{Al}$ interfaces. The overall interfacial thermal conductance calculated from non-equilibrium MD shows that for the $\mathrm{Si}(1\ifmmode\times\else\texttimes\fi{}1)/\mathrm{Al}$ system, the presence of Al twin boundaries can hinder phonon transport and reduce thermal conductance by 2--12% relative to single-crystal Al; whereas the Si $(\sqrt{3}\ifmmode\times\else\texttimes\fi{}\sqrt{3})$ and $(7\ifmmode\times\else\texttimes\fi{}7)$ reconstructions can enhance it by 6--19%. Normal mode decomposition reveals that both the increase and decrease in conductance are related to inelastic phonon scattering. Single-phonon wave-packet simulations predict phonon transport properties consistent with non-equilibrium MD, while further suggesting that phonon polarization conversion is significant even when elastic transmission dominates, and that the interfacial structures have anisotropic impacts on atomic vibrations along different lattice directions. Our findings suggest avenues for achieving selective phonon transport via controlling interfacial structures of materials using atomically precise fabrication techniques, and that the phonon wave-packet formalism is a potentially powerful method for developing a detailed understanding of non-equilibrium phenomena in the low-temperature limit. read less J. S. Shim, D. Go, and H. Beom, “Effects of Geometric and Crystallographic Factors on the Reliability of Al/Si Vertically Cracked Nanofilm/Substrate Systems,” Materials. 2021. link Times cited: 2 Abstract: In this study, tensile tests on aluminum/silicon vertically … read more Abstract: In this study, tensile tests on aluminum/silicon vertically cracked nanofilm/substrate systems were performed using atomistic simulations. Various crystallographic orientations and thicknesses of the aluminum nanofilms were considered to analyze the effects of these factors on the reliability of the nanofilm/substrate systems. The results show that systems with some specific crystallographic orientations have lower reliability compared to the other orientations because of the penetration of the vertical crack into the silicon substrate. This penetration phenomenon occurring in a specific model is related to a high coincidence of atomic matching between the interfaces in the model. This high coincidence leads to a tendency of the interface to maintain a coherent form in which the outermost silicon atoms of the substrate that are bonded to the aluminum nanofilm tend to stick with the aluminum atoms under tensile loads. This phenomenon was verified by interface energy calculations in the simulation models. read less H. Lamsaf et al., “Zn-Fe Flower-like nanoparticles growth by gas condensation,” Materials Letters. 2021. link Times cited: 3 Z. Aitken, V. Sorkin, Z. Yu, S. Chen, Z. Wu, and Y.-W. Zhang, “Modified embedded-atom method potentials for the plasticity and fracture behaviors of unary fcc metals,” Physical Review B. 2021. link Times cited: 5 Z. Lu, A. Chaka, and P. Sushko, “Thermal conductance enhanced via inelastic phonon transport by atomic vacancies at Cu/Si interfaces,” Physical Review B. 2020. link Times cited: 10 J. Ju et al., “First-principles investigations of the stability, electronic structures, mechanical properties and thermodynamic properties of FexAlyCz compounds in Fe-Cr-B-Al-C alloy,” Journal of Physics and Chemistry of Solids. 2020. link Times cited: 12 A. Moitra, “Atomistic simulations of precipitation hardening mechanisms in Mg-Al alloys,” Journal of Physics: Conference Series. 2020. link Times cited: 0 Abstract: Precipitation hardening of Mg-Al alloys primarily comes from… read more Abstract: Precipitation hardening of Mg-Al alloys primarily comes from the interaction of basal dislocations with Mg17Al12 precipitates. Strengthening of Mg-alloys by precipitation is much less efficient than in other metallic alloys (e.g. Al) and this behaviour has been attributed to geometrical efects, as the Mg17Al12 precipitates grow as thin plates/lozenses or long rod shape parallel to the basal plane. In the present study I focus on the dislocation/precipitate interaction in the athermal limit for both edge and screw type basal dislocations, carried out using molecular statics methodology. In particular, the critical resolved shear stress (CRSS) necessary to overcome the precipitates are determined as a function of the precipitate size and compared with predictions of classical continuum models. These results provide valuable information about the precipitate hardening mechanisms and suggested new avenues to improve the mechanical properties of Mg-Al alloys. read less J. Cheng, X. Hu, X. Sun, A. Vivek, G. Daehn, and D. Cullen, “Multi-scale characterization and simulation of impact welding between immiscible Mg/steel alloys,” Journal of Materials Science & Technology. 2020. link Times cited: 12 Y. Ji, C. Dong, D. Kong, and X. Li, “Design materials based on simulation results of silicon induced segregation at AlSi10Mg interface fabricated by selective laser melting,” Journal of Materials Science & Technology. 2020. link Times cited: 30 R. Boom and F. D. Boer, “Enthalpy of formation of binary solid and liquid Mg alloys – Comparison of Miedema-model calculations with data reported in literature,” Calphad-computer Coupling of Phase Diagrams and Thermochemistry. 2020. link Times cited: 4 B. Huddleston et al., “Correlating damage progression to fragmentation at high strain rates using molecular dynamics,” Modelling and Simulation in Materials Science and Engineering. 2020. link Times cited: 1 Abstract: We show a correlation between nanoscale void nucleation and … read more Abstract: We show a correlation between nanoscale void nucleation and the fragment size by employing atomistic simulations that isotropically expand copper with a varying number of uniform grains at various strain rates and temperatures. Damage within the simulation was quantified in terms of the void number density (void nucleation) and void volume. We quantified the fragment size in terms of a length scale parameter defined as the solid volume-per-surface-area. The relationship of the fragment size to the strain rate was compared to existing models and was found to follow a −1/2 power law. At the atomic scale, the void number density is shown to increase with increasing strain rate, increasing temperature, and decreasing grain size. A fundamental relationship between fragmentation and the internal damage structure is suggested by the correlation between the fragment size and the maximum void number density of a −1/3 power law. We can upscale the relationship between void nucleation and fragmentation observed in molecular dynamics to higher length scales by using the length scale-appropriate models for damage evolution. read less I. Aslam et al., “Thermodynamic and kinetic behavior of low-alloy steels: An atomic level study using an Fe-Mn-Si-C modified embedded atom method (MEAM) potential,” Materialia. 2019. link Times cited: 12 A. Nomoev and N. V. Yumozhapova, “Obtaining Composite Nanoparticles via Electron Beam Irradiation and Modeling the Processes of their Formation by Means of Molecular Dynamics,” Bulletin of the Russian Academy of Sciences: Physics. 2019. link Times cited: 0 X. Zhuo, A. Ma, and H. Beom, “Cohesive zone representation of interfacial fracture in aluminum-silicon bimaterials,” Computational Materials Science. 2019. link Times cited: 6 M. Suk and Y.-Y. Kim, “Influence of Deposition Techniques on the Thermal Boundary Resistance of Aluminum Thin-Films,” International Journal of Precision Engineering and Manufacturing. 2019. link Times cited: 2 G. Esteban-Manzanares, A. Ma, I. Papadimitriou, E. Martínez, and J. Llorca, “Basal dislocation/precipitate interactions in Mg–Al alloys: an atomistic investigation,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 19 Abstract: The interaction between edge basal dislocations and β-Mg17Al… read more Abstract: The interaction between edge basal dislocations and β-Mg17Al12 precipitates was studied using atomistic simulations. A strategy was developed to insert a lozenge-shaped Mg17Al12 precipitate with Burgers orientation relationship within the Mg matrix in an atomistic model ensuring that the matrix/precipitate interfaces were close to minimum energy configurations. It was found that the dislocation bypassed the precipitate by the formation of an Orowan loop that entered the precipitate. Within the precipitate, the dislocation was not able to progress further until more dislocations overcome the precipitate and push the initial loop to shear the precipitate along the (110) plane, parallel to the basal plane of Mg. This process was eventually repeated as more dislocations overcome the precipitate and this mechanism of dislocation/precipitate interaction was in agreement with experimental observations. Moreover, the initial resolved shear stress to bypass the precipitate was in agreement with the predictions of the Bacon–Kocks–Scattergood model. read less M. Suk and Y.-Y. Kim, “Influence of Deposition Techniques on the Thermal Boundary Resistance of Aluminum Thin-Films,” International Journal of Precision Engineering and Manufacturing. 2019. link Times cited: 0 T. Li et al., “Effect of trace Ni on the resistance of high-Cr cast iron to slurry erosion,” Wear. 2019. link Times cited: 10 S. E. Muller and A. Nair, “Deformation mechanisms of Al/amorphous-Si core–shell nanorods,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 1 Abstract: In this study, we model the indentation/retraction of an alu… read more Abstract: In this study, we model the indentation/retraction of an aluminum/amorphous-Si (Al/a-Si) core–shell nanorod. We investigate changes in the deformation behavior of the core–shell structure with changes in the size of the core and shell. Since indentation has nonlinear material behavior around the point of contact, and the linear elastic region can span long range, we use a multiscale model as implemented in the coupled atomistic and discrete dislocation (CADD) method. We introduce a two-material/phase formulation of the CADD method to model the fcc Al and a-Si phases. Under indentation and retraction loading, samples with shell show an average 9% greater recovery in core height than samples with no shell. We find that there are three routes for deformation in the core–shell structure: (1) compression of the Al core; (2) deflection of the surrounding Al substrate; and (3) deformation of the a-Si shell. When present, the a-Si shell delocalizes forces generated by the indenter, allowing for all three forms of deformation to be active. This allows the Al core, the a-Si shell, and the surrounding Al substrate to each contribute to the indentation load and increases the force necessary to produce yield in the core. Without the shell, compression of the core is the dominant form of deformation, with yield occurring at lower indentation force than samples with shell. We also find that the dominance of deformation in the core or substrate is dependent on the size of core used. Samples with large cores and thin shells experience deformation mostly in the core. On the other hand, shelled samples with small cores and thick shells have well-protected cores and experience substantial deformation in the substrate. This work will help with the design of low friction nanotextured surfaces composed of core–shell nanorods tailored for specific deformation characteristics. read less H. Jang, S. E. Lee, and J. W. Hong, “Molecular dynamics evaluation of the effects of zinc on the mechanical properties of aluminum alloys,” Computational Materials Science. 2019. link Times cited: 3 S. A. Etesami, M. Laradji, and E. Asadi, “Transferability of interatomic potentials in predicting the temperature dependency of elastic constants for titanium, zirconium and magnesium,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 4 Abstract: We present our investigation of the current state of the art… read more Abstract: We present our investigation of the current state of the art for the transferability of molecular dynamics (MD) interatomic potentials for high temperature simulations of material processes in terms of elastic constants. With the current advancement of computer power, nanoscale computational models such as MD have the potential to accelerate optimization and development of high temperature material processes provided a robust and transferable interatomic potential. Temperature dependency of elastic constants, despite the low temperature elastic constants, is not commonly used as one of the target material properties to develop interatomic potentials for metals; thus, it is a reliable index to determine the transferability of interatomic potentials for high temperature simulations. We consider all five independent elastic constants and their temperature dependency as an index for our evaluations of available interatomic potentials for titanium (Ti), zirconium (Zr), and magnesium (Mg) as representative metals with a relatively complex crystal structure (hcp). The calculated elastic constants and their deviation from their corresponding experimental values are presented. We provide a through discussion on the transferability of each potential and summarize with the most suitable potentials for high temperature material process simulations for each considered material. read less S. Brauer et al., “Multiscale Modeling of Pure Nickel.” 2018. link Times cited: 0 H. Doude et al., “Cast Magnesium Alloy Corvette Engine Cradle.” 2018. link Times cited: 1 F. Wang and B. Li, “Surface and Interfacial Energies of Mg 17 Al 12 –Mg System.” 2018. link Times cited: 1 R. Marimpul, I. Syuhada, A. Rosikhin, and T. Winata, “Effect of deposition rate on melting point of copper film catalyst substrate at atomic scale,” Materials Research Express. 2018. link Times cited: 0 Abstract: Annealing process of copper film catalyst substrate was stud… read more Abstract: Annealing process of copper film catalyst substrate was studied by molcular dynamics simulation. This copper film catalyst substrate was produced using thermal evaporation method. The annealing process was limited in nanosecond order to observe the mechanism at atomic scale. We found that deposition rate parameter affected the melting point of catalyst substrate. The change of crystalline structure of copper atoms was observed before it had been already at melting point. The optimum annealing temperature was obtained to get the highest percentage of fcc structure on copper film catalyst substrate. read less H. Lashgari, C. Tang, D. Chu, and S. Li, “Molecular dynamics simulation of cyclic indentation in Fe-based amorphous alloy,” Computational Materials Science. 2018. link Times cited: 8 N. V. Yumozhapova, A. Nomoev, and Y. Gafner, “Computer Modeling of the Formation Process of Core-Shell Nanoparticles Cu@Si,” Solid State Phenomena. 2018. link Times cited: 3 Abstract: The process of nanoparticle Cu@Si formation by the molecular… read more Abstract: The process of nanoparticle Cu@Si formation by the molecular dynamic method using MEAM-potentials was studied. Modeling the droplet behavior demonstrates that a core-shell structure with a copper core and a silicon shell can be formed if the drop is in the liquid state, until the material is finally redistributed. The parameters of thermal stability of Cu@Si composite nanoparticles of different sizes have been determined. It is concluded that as the temperature increases, the diffusion of copper atoms to the surface begins, which leads to a change in the structure and the formation of particles with a core of the Cu@Si type. read less Y. Gafner, S. Gafner, A. Nomoev, and S. Bardakhanov, “Analysis of the Structure and Thermal Stability of Cu@Si Nanoparticles,” Journal of Metastable and Nanocrystalline Materials. 2018. link Times cited: 1 Abstract: In this research core-shell Cu@Si nanoparticles were obtaine… read more Abstract: In this research core-shell Cu@Si nanoparticles were obtained through evaporation of elemental precursors by a high-powered electron beam. The structures of the particles were investigated in order to elucidate their mechanisms of formation. The thermal stability of the particles was studied with the help of molecular dynamics calculations. The parameters of the thermal stability of the composite nanoparticles Cu@Si with different size were determined. It was concluded that with the temperature increasing the diffusion of copper atoms on the surface begins, leading to a reversal of the structure and the formation of particles having a particle type Si@Cu. read less I. Konovalenko and S. Psakhie, “Molecular dynamics modeling of bonding two materials by atomic scale friction stir welding at different process parameters.” 2017. link Times cited: 11 I. Konovalenko, I. Konovalenko, and S. Psakhie, “Molecular dynamics modeling of bonding two materials by atomic scale friction stir welding.” 2017. link Times cited: 13 H. Hao and D. Lau, “Atomistic modeling of metallic thin films by modified embedded atom method,” Applied Surface Science. 2017. link Times cited: 24 N. Miyazawa, S. Suzuki, M. Mabuchi, and Y. Chino, “Atomic simulations of the effect of Y and Al segregation on the boundary characteristics of a double twin in Mg,” Journal of Applied Physics. 2017. link Times cited: 4 Abstract: There is usually a tradeoff between the strength and the duc… read more Abstract: There is usually a tradeoff between the strength and the ductility in solute strengthening of metallic materials. However, magnesium is an exception. A {10 1¯1}-{10 1¯2} double twin (DT) provides a fracture-initiation site in Mg. Hence, an investigation on effects of segregations on facture at the DT will give a clue for understanding an exception of Mg to the tradeoff relation. In the present work, we investigated segregation behavior of Y and Al at the DT and interactions of a screw dislocation with segregated DTs by Monte Carlo (MC) and molecular dynamics (MD) simulations. The MC simulations showed that the volumes of the atomic Voronoi polyhedron were changed by Y segregation, while the anisotropic factors of the atomic Voronoi polyhedron were changed by Al segregation. Also, the MD simulations indicated that Y segregation induced emission of twinning dislocations from the DT, while Al segregation suppressed the motion of the twinning dislocation. Thus, the boundary characteristics of the Y-segregated... read less Y. Sato, C. Nakai, M. Wakeda, and S. Ogata, “Predictive modeling of Time-Temperature-Transformation diagram of metallic glasses based on atomistically-informed classical nucleation theory,” Scientific Reports. 2017. link Times cited: 15 J. Luo, Y. Jiang, R. Yu, and Y. Wu, “The competition of densification and structure ordering during crystallization of HCP-Mg in the framework of layering,” Chemical Physics Letters. 2017. link Times cited: 3 K. Kim and B.-J. Lee, “Modified embedded-atom method interatomic potentials for Mg-Nd and Mg-Pb binary systems,” Calphad-computer Coupling of Phase Diagrams and Thermochemistry. 2017. link Times cited: 12 S. Sajadi, S. Hocker, A. Mora, P. Binkele, J. Seeger, and S. Schmauder, “Precipitation in a copper matrix modeled by ab initio calculations and atomistic kinetic Monte Carlo simulations,” physica status solidi (b). 2017. link Times cited: 6 Abstract: A kinetic Monte Carlo approach is used to study the influenc… read more Abstract: A kinetic Monte Carlo approach is used to study the influence of Cr, Fe, Al, or Mg addition on the precipitation in a Cu–Ni–Si alloy. The simulation method is based on a vacancy diffusion model. The crucial parameters of this method are the pairwise mixing energies of all contained elements which are determined by ab initio calculations. The number of dissolved atoms in equilibrium state is used to estimate the influence of Cr, Fe, Al, or Mg on the electrical conductivity. In order to estimate the influence of the alloying elements on strength ab initio calculations of misfit strain at the Cu/Ni3 Si interface are performed. The atomistic kinetic Monte Carlo (AKMC) simulations reveal that Cr, Fe, Al, and Mg atoms are located at different positions: Mg and Al atoms are preferably located at the interface of Ni–Si precipitates and Cu matrix, Cr atoms diffuse into Ni–Si precipitates, and Fe atoms form Fe clusters surrounded by Ni–Si shells. Cr addition leads to a significantly reduced fraction of atoms dissolved in the matrix which indicates an increase of electrical conductivity, whereas Mg addition results in a high misfit strain at the Cu/Ni3 Si interface which can contribute to increased strength. read less R. Marimpul, I. Syuhada, A. Rosikhin, and T. Winata, “Effect of copper film catalyst substrate thickness on atomic diffusion time at the initiation of the recrystallization stage: a molecular dynamics study,” Materials Research Express. 2017. link Times cited: 1 Abstract: Copper film growth using thermal evaporation and annealing m… read more Abstract: Copper film growth using thermal evaporation and annealing methods were studied using molecular dynamics simulations. The AlSiMgCuFe modified embedded atom method potential was used to describe the interaction of Cu–Cu, Si–Si and Cu–Si atoms. The annealing process, which was limited to atomic diffusion, repaired the crystal structure of the copper film. Our results showed that the thickness of the copper film catalyst substrate affected the initiation of the recrystallization process. A change of phase transition of copper atoms was observed after annealing. These phenomena were supported by knowledge of the radial distribution function and analysis of the crystal structure. read less M. Guerdane, “Self-diffusion in intermetallic AlAu4: Molecular dynamics study down to temperatures relevant to wire bonding,” Computational Materials Science. 2017. link Times cited: 5 Y. Cui and Z. Chen, “Void initiation from interfacial debonding of spherical silicon particles inside a silicon-copper nanocomposite: a molecular dynamics study,” Modelling and Simulation in Materials Science and Engineering. 2017. link Times cited: 15 Abstract: Silicon particles with diameters from 1.9 nm to 30 nm are em… read more Abstract: Silicon particles with diameters from 1.9 nm to 30 nm are embedded in a face-centered-cubic copper matrix to form nanocomposite specimens for simulation. The interfacial debonding of silicon particles from the copper matrix and the subsequent growth of nucleated voids are studied via molecular dynamics (MD). The MD results are examined from several different perspectives. The overall mechanical performance is monitored by the average stress–strain response and the accumulated porosity. The ‘relatively farthest-traveled’ atoms are identified to characterize the onset of interfacial debonding. The relative displacement field is plotted to illustrate both subsequent interfacial debonding and the growth of a nucleated void facilitated by a dislocation network. Our results indicate that the initiation of interfacial debonding is due to the accumulated surface stress if the matrix is initially dislocation-free. However, pre-existing dislocations can make a considerable difference. In either case, the dislocation emission also contributes to the subsequent debonding process. As for the size effect, the debonding of relatively larger particles causes a drop in the stress–strain curve. The volume fraction of second-phase particles is found to be more influential than the size of the simulation box on the onset of interfacial debonding. The volume fraction of second-phase particles also affects the shape of the nucleated void and, therefore, influences the stress response of the composite. read less D. Johansson, P. Hansson, and S. Melin, “Lattice optimization of Si-Cu interfaces on the atomic scale,” Computational Materials Science. 2017. link Times cited: 2 D. Dickel, T. Tenev, P. M. Gullett, and M. Horstemeyer, “The notion of a plastic material spin in atomistic simulations,” Modelling and Simulation in Materials Science and Engineering. 2016. link Times cited: 0 Abstract: A kinematic algorithm is proposed to extend existing constru… read more Abstract: A kinematic algorithm is proposed to extend existing constructions of strain tensors from atomistic data to decouple elastic and plastic contributions to the strain. Elastic and plastic deformation and ultimately the plastic spin, useful quantities in continuum mechanics and finite element simulations, are computed from the full, discrete deformation gradient and an algorithm for the local elastic deformation gradient. This elastic deformation gradient algorithm identifies a crystal type using bond angle analysis (Ackland and Jones 2006 Phys. Rev. B 73 054104) and further exploits the relationship between bond angles to determine the local deformation from an ideal crystal lattice. Full definitions of plastic deformation follow directly using a multiplicative decomposition of the deformation gradient. The results of molecular dynamics simulations of copper in simple shear and torsion are presented to demonstrate the ability of these new discrete measures to describe plastic material spin in atomistic simulation and to compare them with continuum theory. read less M. Zacate, “Indium-defect interactions in FCC and BCC metals studied using the modified embedded atom method,” Hyperfine Interactions. 2016. link Times cited: 0 N. Burbery, R. Das, and W. Ferguson, “Thermo-kinetic mechanisms for grain boundary structure multiplicity, thermal instability and defect interactions,” Materials Chemistry and Physics. 2016. link Times cited: 9 W. Fang, H. Xie, F. Yin, J. Li, D. Khan, and F. Qian, “Molecular dynamics simulation of grain boundary geometry on crack propagation of bi-crystal aluminum,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2016. link Times cited: 39 L. You, L. Hu, Y. Xie, and S. Zhao, “Influence of Cu precipitation on tensile properties of Fe–Cu–Ni ternary alloy at different temperatures by molecular dynamics simulation,” Computational Materials Science. 2016. link Times cited: 14 S. Xiao, Y. Kong, Y. Qiu, and Y. Du, “The microstructure evolution of U1 and U2 nanowires constrained in Al matrix,” Computational Materials Science. 2016. link Times cited: 4 N. Burbery, R. Das, and W. Ferguson, “Transitional grain boundary structures and the influence on thermal, mechanical and energy properties from molecular dynamics simulations,” Acta Materialia. 2016. link Times cited: 11 Y. Qiu, Y. Kong, S. Xiao, and Y. Du, “Mechanical properties of β″ precipitates containing Al and/or Cu in age hardening Al alloys,” Journal of Materials Research. 2016. link Times cited: 6 Abstract: Evidences show that the composition of β″ formed in age hard… read more Abstract: Evidences show that the composition of β″ formed in age hardening of Al alloys should be the prototype Mg_5Si_6 with Al and/or Cu addition. In the present work, molecular dynamics simulations are carried out to investigate the influence of the addition of Al and/or Cu to the mechanical properties of the prototype Mg_5Si_6. Our simulations imply that Mg_5Si_6 with both Al and Cu addition has relatively poor mechanical performance when compared with other three models. The snapshots of atomic configurations during uniaxial tension test illustrate that only if both Al and Cu dissolve in β″, clusters can form through Al atoms segregating around Cu atoms, thus applying different stress fields on the Al matrix, resulting different mechanical properties in comparison with other three β″ models. read less J. Dziedzic, S. Winczewski, and J. Rybicki, “Structure and properties of liquid Al–Cu alloys: empirical potentials compared,” Computational Materials Science. 2016. link Times cited: 17 A. F. Hidayat, A. Rosikhin, I. Syuhada, and T. Winata, “Effects of temperature on Cu structure deposited on Si substrate: A molecular dynamics study.” 2016. link Times cited: 2 Abstract: The deposition process of copper onto silicon substrate was … read more Abstract: The deposition process of copper onto silicon substrate was studied by the molecular dynamics method. Tersoff, MEAM, and Morse potentials were used to describe the interaction of Si-Si Cu-Cu, and Cu-Si, respectively. Deposition process was performed using NVE ensemble and applying Berendsen thermostat with 0,2 fs timestep for 100 ps. The effect of substrate temperature on the percentage of amorphous structure, radial distribution function (RDF), and coordination number was investigated. The result was indicated that at 300 K, the percentage of amorphous structure was relatively lower compared to another temperature. First peaks of RDF at each temperature were found at radius 3,05 A and were still relatively wide, indicating short-range order structure. read less E. Asadi and M. A. Zaeem, “Predicting Solidification Properties of Magnesium by Molecular Dynamics Simulations.” 2016. link Times cited: 0 I. Konovalenko, I. Konovalenko, A. Dmitriev, S. Psakhie, and E. Kolubaev, “Mass Transfer at Atomic Scale in MD Simulation of Friction Stir Welding,” Key Engineering Materials. 2016. link Times cited: 7 Abstract: Mass transfer has been studied at atomic scale by molecular … read more Abstract: Mass transfer has been studied at atomic scale by molecular dynamics simulation of friction stir welding and vibration-assisted friction stir welding using the modified embedded atom potential. It was shown that increasing the velocity movement and decreasing the angle velocity of the tool reduce the penetration depth of atoms into the opposite crystallite in the connected pair of metals. It was shown also that increasing the amplitude of vibrations applied to the friction stir welding tool results in increasing the interpenetration of atoms belonging to the crystallites joined read less N. Miyazawa, T. Yoshida, M. Yuasa, Y. Chino, and M. Mabuchi, “Effect of segregated Al on and twinning in Mg,” Journal of Materials Research. 2015. link Times cited: 9 M. I. Pascuet and J. R. Fernández, “Atomic interaction of the MEAM type for the study of intermetallics in the Al–U alloy,” Journal of Nuclear Materials. 2015. link Times cited: 37 M. Ganchenkova et al., “Influence of the ab-initio calculation parameters on prediction of energy of point defects in silicon,” Modern Electronic Materials. 2015. link Times cited: 7 B. Liu, H. Zhang, J. Tao, X. Chen, and Y.-A. Zhang, “Comparative investigation of a newly optimized modified embedded atom method potential with other potentials for silicon,” Computational Materials Science. 2015. link Times cited: 7 J.-yu Yang, W. Hu, and J.-feng Tang, “Influence of solid–liquid interface on the thermal stability of Li–Fe nanoalloy with rhombohedral structure: A molecular dynamics study,” Thin Solid Films. 2015. link Times cited: 7 I. Konovalenko and I. Konovalenko, “Influence of tool shape on lattice rearrangement under loading conditions reproducing friction stir welding.” 2015. link Times cited: 1 Abstract: Metal behavior under loading conditions that reproduce frict… read more Abstract: Metal behavior under loading conditions that reproduce friction stir welding was studied on the atomic scale. Calculations were conducted based on molecular dynamics simulation with potentials calculated within the embedded atom method. The loading of the interface between two crystallites, whose structure corresponded to aluminum alloy 2024, was simulated by the motion of a cone-shaped tool along the interface with constant angular and translational velocities. The motion of the rotating tool causes fracture of the workpiece crystal structure with subsequent mixing of surface atoms of the interfacing crystallites. It is shown that the resistance force acting on the moving tool from the workpiece and the process of structural defect formation in the workpiece depend on the tool shape. read less I. Konovalenko, I. Konovalenko, A. Dmitriev, S. Psakhie, and E. A. Kolubaev, “Influence of vibrational treatment on thermomechanical response of material under conditions identical to friction stir welding.” 2015. link Times cited: 0 Abstract: A molecular dynamics model was constructed to describe mater… read more Abstract: A molecular dynamics model was constructed to describe material loading on the atomic scale by the mode identical to friction stir welding. It was shown that additional vibration applied to the tool during the loading mode provides specified intensity values and continuous thermomechanical action during welding. An increase in additional vibration intensity causes an increase both in the force acting on the workpiece from the rotating tool and in temperature within the welded area. read less E. Hahn and M. Meyers, “Grain-size dependent mechanical behavior of nanocrystalline metals,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2015. link Times cited: 162 L. Deng, H. Deng, J.-feng Tang, X. Zhang, S. Xiao, and W. Hu, “Monte Carlo simulations of strain-driven elemental depletion or enrichment in Cu95Al5 and Cu90Al10 alloys,” Computational Materials Science. 2015. link Times cited: 1 M. Alam and S. Groh, “Dislocation modeling in bcc lithium: A comparison between continuum and atomistic predictions in the modified embedded atoms method,” Journal of Physics and Chemistry of Solids. 2015. link Times cited: 19 X. Y. Zhang, B. Li, and Q. Liu, “Non-equilibrium basal stacking faults in hexagonal close-packed metals,” Acta Materialia. 2015. link Times cited: 72 X. Hu and A. Martini, “Atomistic simulation of the effect of roughness on nanoscale wear,” Computational Materials Science. 2015. link Times cited: 32 E. Asadi, M. A. Zaeem, S. Nouranian, and M. Baskes, “Two-Phase Solid-Liquid Coexistence of Ni, Cu, and Al by Molecular Dynamics Simulations using the Modified Embedded-Atom Method,” Acta Materialia. 2015. link Times cited: 94 E. Asadi, M. A. Zaeem, S. Nouranian, and M. Baskes, “Quantitative Modeling of the Equilibration of Two-Phase Solid-Liquid Fe by Atomistic Simulations on Diffusive Time Scales,” Physical Review B. 2015. link Times cited: 61 Abstract: (Received 10 July 2014; revised manuscript received 10 Decem… read more Abstract: (Received 10 July 2014; revised manuscript received 10 December 2014; published 12 January 2015) In this paper, molecular dynamics (MD) simulations based on the modified-embedded atom method (MEAM) and a phase-field crystal (PFC) model are utilized to quantitatively investigate the solid-liquid properties of Fe. A set of second nearest-neighbor MEAM parameters for high-temperature applications are developed for Fe, and the solid-liquid coexisting approach is utilized in MD simulations to accurately calculate the melting point, expansion in melting, latent heat, and solid-liquid interface free energy, and surface anisotropy. The required input properties to determine the PFC model parameters, such as liquid structure factor and fluctuations of atoms in the solid, are also calculated from MD simulations. The PFC parameters are calculated utilizing an iterative procedure from the inputs of MD simulations. The solid-liquid interface free energy and surface anisotropy are calculated using the PFC simulations. Very good agreement is observed between the results of our calculations from MEAM-MD and PFC simulations and the available modeling and experimental results in the literature. As an application of the developed model, the grain boundary free energy of Fe is calculated using the PFC model and the results are compared against experiments. read less S. Zhang et al., “Preparation and wear properties of TiB2/Al–30Si composites via in-situ melt reactions under high-energy ultrasonic field,” Transactions of Nonferrous Metals Society of China. 2014. link Times cited: 16 S. Groh, “Transformation of shear loop into prismatic loops during bypass of an array of impenetrable particles by edge dislocations,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2014. link Times cited: 15 M. Ganchenkova, Y. Yagodzinskyy, V. Borodin, and H. Hänninen, “Effects of hydrogen and impurities on void nucleation in copper: simulation point of view,” Philosophical Magazine. 2014. link Times cited: 31 Abstract: The mechanisms of hydrogen influence on vacancy cluster form… read more Abstract: The mechanisms of hydrogen influence on vacancy cluster formation in copper are studied using numerical simulations. Vacancy agglomeration in clusters larger than divacancies is found to be energetically favourable, but in pure copper the cluster creation is prevented by the lack of binding between single vacancies. Hydrogen dissolved in the lattice readily accumulates in vacancy-type defects, changing their properties. A single vacancy can accommodate up to six hydrogen atoms. Hydrogen stabilizes divacancies and promotes vacancy cluster nucleation. In larger vacancy clusters, accumulated hydrogen prevents cluster collapse into stacking fault tetrahedra. In small voids, hydrogen prefers to remain in atomic form at the void surface, but when voids become sufficiently large, hydrogen molecules in the void interior can also be formed. Some common impurities in copper (O, S, P and Ag) contribute to void formation by capturing vacancies in their vicinity. In contrast, substitutional Ni has little effect on vacancy clustering but tends to capture interstitial hydrogen. read less C. Zhang, S. Huang, J. Shen, and N. Chen, “Structural and mechanical properties of Fe–Al compounds: An atomistic study by EAM simulation,” Intermetallics. 2014. link Times cited: 43 A. Moitra, S.-G. Kim, and M. Horstemeyer, “Solute effect on the 〈a+c〉 dislocation nucleation mechanism in magnesium,” Acta Materialia. 2014. link Times cited: 58 G. Sushko, A. Verkhovtsev, A. Yakubovich, S. Schramm, and A. Solov’yov, “Molecular dynamics simulation of self-diffusion processes in titanium in bulk material, on grain junctions and on surface.,” The journal of physical chemistry. A. 2014. link Times cited: 16 Abstract: The process of self-diffusion of titanium atoms in a bulk ma… read more Abstract: The process of self-diffusion of titanium atoms in a bulk material, on grain junctions and on surface is explored numerically in a broad temperature range by means of classical molecular dynamics simulation. The analysis is carried out for a nanoscale cylindrical sample consisting of three adjacent sectors and various junctions between nanocrystals. The calculated diffusion coefficient varies by several orders of magnitude for different regions of the sample. The calculated values of the bulk diffusion coefficient correspond reasonably well to the experimental data obtained for solid and molten states of titanium. Investigation of diffusion in the nanocrystalline titanium is of a significant importance because of its numerous technological applications. This paper aims to reduce the lack of data on diffusion in titanium and describe the processes occurring in bulk, at different interfaces and on surface of the crystalline titanium. read less M. Liao, B. Li, and M. Horstemeyer, “Interaction between prismatic slip and a Mg17Al12 precipitate in magnesium,” Computational Materials Science. 2013. link Times cited: 29 R. Li, Y. Zhong, C.-H. Huang, X. Tao, and Y. Ouyang, “Surface energy and surface self-diffusion of Al calculated by embedded atom method,” Physica B-condensed Matter. 2013. link Times cited: 9 M. Liao, B. Li, and M. Horstemeyer, “Unstable dissociation of a prismatic dislocation in magnesium,” Scripta Materialia. 2013. link Times cited: 13 F. Wu, Q. Zhang, X. Jin, Y. Jiang, and S. Z. Li, “A Score-based Geometric Model for Molecular Dynamics Simulations,” ArXiv. 2022. link Times cited: 7 Abstract: Molecular dynamics (MD) has long been the de facto choice fo… read more Abstract: Molecular dynamics (MD) has long been the de facto choice for modeling complex atomistic systems from ﬁrst principles, and recently deep learning become a popular way to accelerate it. Notwithstanding, preceding approaches depend on intermediate variables such as the potential energy or force ﬁelds to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called ScoreMD by directly estimating the gradient of the log density of molecular conformations. Moreover, we analyze that diffusion processes highly accord with the principle of enhanced sampling in MD simulations, and is therefore a perfect match to our sequential conformation generation task. That is, ScoreMD perturbs the molecular structure with a conditional noise depending on atomic accelerations and employs conformations at previous timeframes as the prior distribution for sampling. Another challenge of modeling such a conformation generation process is that the molecule is kinetic instead of static, which no prior studies strictly consider. To solve this challenge, we introduce a equivariant geometric Transformer as a score function in the diffusion process to calculate the corresponding gradient. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperforms state-of-the-art baselines on MD17 and isomers of C7O2H10. This research provides new insights into the acceleration of new material and drug discovery. read less Z. Xing, H. Fan, J.-J. Tang, B. Wang, and G. Kang, “Molecular dynamics simulation on the cyclic deformation of magnesium single crystals,” Computational Materials Science. 2021. link Times cited: 22 S. Suresh, M. J. Echeverría, and A. Dongare, “Atomistic study of silicon alloying in the spallation behavior of nanocrystalline aluminum systems.” 2020. link Times cited: 1 D. Rapp et al., “Multiscale Simulation of Precipitation in Copper-Alloyed Pipeline Steels and in Cu-Ni-Si Alloys,” Handbook of Mechanics of Materials. 2019. link Times cited: 0 M. Khalid, J. Friis, P. H. Ninive, K. Marthinsen, and A. Strandlie, “DFT calculations based insight into bonding character and strength of Fe2Al5 and Fe4Al13 intermetallics at Al-Fe joints,” Procedia Manufacturing. 2018. link Times cited: 18 H. Mori and N. Matubayasi, “MD simulation analysis of resin filling into nano-sized pore formed on metal surface,” Applied Surface Science. 2018. link Times cited: 13 V. S. Baidyshev and E. A. Khartavich, “Theoretical Investigations of Synthesis Mechanisms of Gas-phase Bicomponent Nanoparticles Cu@Si.” 2016. link Times cited: 0 Abstract: In the work the condensation process Cu Si has been investig… read more Abstract: In the work the condensation process Cu Si has been investigated by the molecular dynamics method with of MEAM – potentials. It has been revealed that the atomic vapor form only alloy particles at homogeneous condensation. It has been determined that one of possible mechanisms for the formation of partially coated Si nanoparticle deposition mechanism of Cu is small Si clusters formed Cu metal core. read less D. Johansson and P. Hansson, “SHEAR ANISOTROPY IN Si-Cu INTERFACES ON THE ATOMIC SCALE,” Computational Materials Science. 2016. link Times cited: 0 Abstract: Three dimensional molecular dynamics (MD) is used to model t… read more Abstract: Three dimensional molecular dynamics (MD) is used to model the mechanical response at the interface between a thin Cu coating resting on a base of Si. The copper coating is subjected to a displacement controlled shear load and the atom movements at the Si-Cu interface are monitored to investigate the effects of crystallographic anisotropy. The two crystals have the same crystallographic orientation, and two different interface normal directions are considered. The shear load is applied along different crystallographic directions to highlight the importance of crystallographic orientation for the mechanical response. The simulations are performed with the 3D MD free-ware LAMMPS. As the imposed displacement reaches a high enough magnitude, the Cu coating starts to slide over the Si base. Thus the atoms at the interface rearrange depending on loading direction and crystallographic orientation. (Less) read less J. M. Hughes et al., “Hierarchical Bridging Between Ab Initio and Atomistic Level Computations: Sensitivity and Uncertainty Analysis for the Modified Embedded-Atom Method (MEAM) Potential (Part B),” JOM. 2015. link Times cited: 15 M. Horstemeyer et al., “Hierarchical Bridging Between Ab Initio and Atomistic Level Computations: Calibrating the Modified Embedded Atom Method (MEAM) Potential (Part A),” JOM. 2015. link Times cited: 13 J.-yu Yang, W. Hu, and J.-feng Tang, “Effect of incident energy on the configuration of Fe–Al nanoparticles, a molecular dynamics simulation of impact deposition,” RSC Advances. 2014. link Times cited: 10 Abstract: The impact deposition of Al (or Fe) atoms on the rhombohedro… read more Abstract: The impact deposition of Al (or Fe) atoms on the rhombohedron of Fe (or the truncated octahedron of Al) nanoparticles is investigated by performing a molecular dynamics simulation using the embedded atom method. These simulations are performed in different incident energies (from 10 eV to 50 eV). The dependence of the incident energy of deposited atoms on the growth configurations of Fe–Al nanoparticles is analyzed. For the deposition of Al atoms on the Fe nanoparticle, some Al atoms are incorporated into the Fe core as the incident energy of Al increases. A nanoparticle configuration with Fe-core and Al-shell is usually observed at all incident energies considered. In this case, the substrate Fe atoms and the deposited Al atoms are arranged in body-centered cubic configuration. For the impact deposition of Fe atoms on the Al nanoparticle, an onion-like nanoparticle is observed at incident energy of 10 eV. A configuration with Al-shell and alloyed Fe–Al core is obtained as the incident energy increases. This study proposes a method of artificially controlling nanoalloy configuration. read less P. Rico and P. Antonio, “Relaxation processes in Cu-Zr metallic glasses by molecular dynamics simulations.” 2014. link Times cited: 0 Abstract: Παρουσιάζουμε αποτελέσματα προσομοιώσεων Μοριακής Δυναμικής … read more Abstract: Παρουσιάζουμε αποτελέσματα προσομοιώσεων Μοριακής Δυναμικής σχετικά με τις διαδικασίες εφησυχασμών που συμβαίνουν στη Μεταλλική Ύαλο Cu₆₅Zr₃₅ σε κατάσταση ηρεμίας, κατά τη διάρκεια της εφελκυστικής τάσης, καθώς και την απελευθέρωση τάσης που λαμβάνει χώρα όταν το υλικό αυτό είναι υπό σταθερή πίεση. Η μελέτη περιλαμβάνει μια διεξοδική ανάλυση των θερμοδυναμικών, δομικών και δυναμικών ιδιοτήτων αυτού του συστήματος. Βρήκαμε έναν δονητικό τρόπο των ατομικών κινήσεων που λαμβάνει χώρα σε ιδιαίτερα τοπικές περιοχές που χαρακτηρίζονται από την υψηλή τους κινητικότητα και τη χαμηλή τοπική πυκνότητα και αποτελούνται κυρίως από χαλαρά συσσωρευμένες ομάδες ατόμων. Ο δονητικός αυτός τρόπος έχει μια συχνότητα που ουσιαστικά είναι ανεξάρτητη τόσο από τη θερμοκρασία όσο και από την εφαρμοζόμενη ένταση. Ωστόσο, οι περιοχές όπου εντοπίζονται τα ταλαντευόμενα άτομα αυξάνονται με τη θερμοκρασία και την εφαρμοζόμενη ένταση. Υποστηρίζεται ότι οι κινήσεις αυτές αποτελούν τις «γρήγορες διαδικασίες» που βρίσκονται μεταξύ της χαλάρωσης β και της κορυφής του Boson στο χαρακτηριστικό φάσμα των δυναμικών τρόπων λειτουργίας στα γυαλιά. Επιπλέον, διαπιστώσαμε ότι η ύαλος αυτή παρουσίασε μια χαρακτηριστική ενδοθερμική απόκριση κατά τον εφελκυσμό η οποία δεν έχει αναφερθεί προηγουμένως. Η μελέτη αυτής της απόκρισης και η εξέλιξή της κατά τη διέγερση δείχνει την παρουσία διαδικασιών γήρανσης και αναζωογόνησης και οριοθετεί τα διαφορετικά στάδια στα οποία κυριαρχεί η καθεμία από αυτές τις διεργασίες στο γυαλί μας. Συμπεραίνεται ότι οι γρήγορες διεργασίες είναι πρόδρομοι του τρόπου β και θα μπορούσαν να θεωρηθούν ως παράγοντες πρόβλεψης της χωρικής προέλευσης των ζωνών μετασχηματισμού διάτμησης που διέπουν την έναρξη της πλαστικής ροής. Περαιτέρω, κατά την διάρκεια της διαδικασίας χαλάρωσης του στρες που συμβαίνει όταν το γυαλί υποβάλλεται σε σταθερή τάση, η θέση των ταλαντευόμενων ατόμων συμπίπτει με τις ετερογενείς περιοχές όπου απελευθερώνεται το μεγαλύτερο μέρος του στρες και της ενέργειας του συστήματος και επομένως δρουν ως διαμεσολαβητές στρες των μεταλλικών γυαλιών. Αυτά τα αποτελέσματα θα μπορούσαν να είναι χρήσιμα για την κατανόηση του σύνθετου φάσματος χαλάρωσης των μεταλλικών γυαλιών και να παρέχουν μια καλύτερη εικόνα της περίπλοκης δυναμικής χαλάρωσης που χαρακτηρίζει αυτά τα υλικά, ειδικά στην περιοχή υψηλότερων συχνοτήτων. Επιπλέον, τα αποτελέσματα που παρουσιάζονται εδώ έχουν σημαντικές συνέπειες στις μακροσκοπικές ιδιότητες των μεταλλικών γυαλιών, όπως η ολκιμότητα, η ευθραυστότητα και το κάταγμα της σκληρότητας. Αποδεικνύεται λοιπόν ότι η κατανόηση της δομικής και δυναμικής συμπεριφοράς των MG κατά τη διάρκεια αυτών των διαδικασιών χαλάρωσης είναι ζωτικής σημασίας για τον ενδεχόμενο έλεγχο και βελτίωση των ιδιοτήτων τους. read less B. Yao, Z. R. Liu, and R. F. Zhang, “EAPOTc: An integrated empirical interatomic potential optimization platform for compound solids,” Computational Materials Science. 2022. link Times cited: 1 Y. Zhang, W. Sun, and J. Chen, “Application of Embedded Smart Wearable Device Monitoring in Joint Cartilage Injury and Rehabilitation Training,” Journal of Healthcare Engineering. 2022. link Times cited: 2 Abstract: Joint injuries cause varying degrees of damage to joint cart… read more Abstract: Joint injuries cause varying degrees of damage to joint cartilage. The purpose of this paper is to study the application of embedded smart wearable device monitoring in articular cartilage injury and rehabilitation training. This paper studies what an embedded system is and what a smart wearable device is and also introduces the rehabilitation training method of articular cartilage injury. We cited an embedded matching cost algorithm and an improved AD-Census. The joint cartilage damage and rehabilitation training are monitored. Finally, we introduced the types of smart wearable devices and different types of application fields. The results of this paper show that, after an articular cartilage injury, the joint function significantly recovers using the staged exercise rehabilitation training based on embedded smart wearable device monitoring. We concluded that, from 2013 to 2020, smart wearable devices are very promising in the medical field. In 2020, the value will reach 20 million US dollars. read less G. G. Varenikov, I. Novoselov, and E. Meshkov, “Novel method for automatic search for stable ordered phases in multicomponent systems,” Computational Materials Science. 2021. link Times cited: 2 C. M. Andolina, J. G. Wright, N. Das, and W. Saidi, “Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential,” Physical Review Materials. 2021. link Times cited: 14 Abstract: Various industrial/commercial applications use Al-Mg alloys,… read more Abstract: Various industrial/commercial applications use Al-Mg alloys, yet the Mg added to Al materials, to improve strength, is susceptible to surface segregation and oxidation, leaving behind a softer and Al-enriched bulk alloy. To better understand this process and provide a systematic methodology for investigating dopants that can mitigate corrosion, we have developed a robust atomistic deep neural net potential (DNP) using a dataset generated with first-principles density-functional theory (DFT). The potential, validated systematically against DFT values, has been shown to have a high fidelity in calculating different elemental and intermetallic Al-Mg systems' properties. Our calculations predict a linear trend in the formation energy of the Al-Mg alloy and its density as a function of temperature, consistent with experimental literature. Employing the DNP within a hybrid Monte Carlo and molecular dynamics (MC/MD) approach, we predict anisotropic surface segregation for Al-Mg alloys such that (111)(100)(110), with (111) surfaces displaying the lowest segregation enthalpies and Mg enrichment. Furthermore, we model the segregation tendencies by adapting a recently introduced isotherm model for grain boundary segregation. Our results show that this model describes the MC/MD segregation profiles with higher fidelity than the McLean and Fowler-Guggenheim isotherm models. read less X. Chen et al., “Machine learning enhanced empirical potentials for metals and alloys,” Comput. Phys. Commun. 2021. link Times cited: 5 X. Chen, X. Gao, Y. Zhao, D. Lin, W. Chu, and H. Song, “TensorAlloy: An automatic atomistic neural network program for alloys,” Comput. Phys. Commun. 2020. link Times cited: 10 C. Cheng et al., “Development and application of EAM potentials for Ti, Al and Nb with enhanced planar fault energy of Ti,” Computational Materials Science. 2020. link Times cited: 4 J. Harrison, J. Schall, S. Maskey, P. Mikulski, M. T. Knippenberg, and B. Morrow, “Review of force fields and intermolecular potentials used in atomistic computational materials research,” Applied Physics Reviews. 2018. link Times cited: 99 Abstract: Molecular simulation is a powerful computational tool for a … read more Abstract: Molecular simulation is a powerful computational tool for a broad range of applications including the examination of materials properties and accelerating drug discovery. At the heart of molecular simulation is the analytic potential energy function. These functions span the range of complexity from very simple functions used to model generic phenomena to complex functions designed to model chemical reactions. The complexity of the mathematical function impacts the computational speed and is typically linked to the accuracy of the results obtained from simulations that utilize the function. One approach to improving accuracy is to simply add more parameters and additional complexity to the analytic function. This approach is typically used in non-reactive force fields where the functional form is not derived from quantum mechanical principles. The form of other types of potentials, such as the bond-order potentials, is based on quantum mechanics and has led to varying levels of accuracy and transferability. When selecting a potential energy function for use in molecular simulations, the accuracy, transferability, and computational speed must all be considered. In this focused review, some of the more commonly used potential energy functions for molecular simulations are reviewed with an eye toward presenting their general forms, strengths, and weaknesses.Molecular simulation is a powerful computational tool for a broad range of applications including the examination of materials properties and accelerating drug discovery. At the heart of molecular simulation is the analytic potential energy function. These functions span the range of complexity from very simple functions used to model generic phenomena to complex functions designed to model chemical reactions. The complexity of the mathematical function impacts the computational speed and is typically linked to the accuracy of the results obtained from simulations that utilize the function. One approach to improving accuracy is to simply add more parameters and additional complexity to the analytic function. This approach is typically used in non-reactive force fields where the functional form is not derived from quantum mechanical principles. The form of other types of potentials, such as the bond-order potentials, is based on quantum mechanics and has led to varying levels of accuracy and transferabilit... read less C. Barrett, H. Kadiri, and R. Moser, “Generalized interfacial fault energies,” International Journal of Solids and Structures. 2017. link Times cited: 9 J. Svoboda and F. Fischer, “A Self-Consistent Model for Thermodynamics of Multicomponent Solid Solutions,” Scripta Materialia. 2016. link Times cited: 2 B. Liu, H. Zhang, J. Tao, Z. R. Liu, X. Chen, and Y. Zhang, “Development of a second-nearest-neighbor modified embedded atom method potential for silicon–phosphorus binary system,” Computational Materials Science. 2016. link Times cited: 8 I. Hijazi and Y. H. Park, “Mixed intermetallic potentials for Fe-Cu compounds,” Molecular Simulation. 2016. link Times cited: 0 Abstract: Metastable Fe-Cu alloys are of considerable scientific inter… read more Abstract: Metastable Fe-Cu alloys are of considerable scientific interest, and an efficient interatomic potential is crucial for reliable atomistic simulations. Interatomic potentials developed for pure Fe and pure Cu are difficult to mix for the Fe-Cu alloys since the analytic function form of pure Fe is not of the same type of pure Cu potential. Additionally, elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimised separately. In this article, we present compatible analytic function forms that work for pure species and are easily mixed with two adjustable parameters for their alloys. We tested the proposed potentials to make sure that the performance is adequate for pure species as well as their alloys. The predicted values were in good agreement with experimental results. read less A. Akimov and O. Prezhdo, “Large-Scale Computations in Chemistry: A Bird’s Eye View of a Vibrant Field.,” Chemical reviews. 2015. link Times cited: 171 S. Groh, “Mechanical, thermal, and physical properties of Mg-Ca compounds in the framework of the modified embedded-atom method.,” Journal of the mechanical behavior of biomedical materials. 2015. link Times cited: 16 M. Trochet, F. Berthier, and P. Pernot, “Sensitivity analysis and uncertainty propagation for SMA-TB potentials,” Computational Materials Science. 2022. link Times cited: 1 J. S. Shim, G. H. Lee, C. Cui, and H. Beom, “Mechanical Behaviors of Si/CNT Core/Shell Nanocomposites under Tension: A Molecular Dynamics Analysis,” Nanomaterials. 2021. link Times cited: 3 Abstract: The silicon/carbon nanotube (core/shell) nanocomposite elect… read more Abstract: The silicon/carbon nanotube (core/shell) nanocomposite electrode model is one of the most promising solutions to the problem of electrode pulverization in lithium-ion batteries. The purpose of this study is to analyze the mechanical behaviors of silicon/carbon nanotube nanocomposites via molecular dynamics computations. Fracture behaviors of the silicon/carbon nanotube nanocomposites subjected to tension were compared with those of pure silicon nanowires. Effective Young’s modulus values of the silicon/carbon nanotube nanocomposites were obtained from the stress and strain responses and compared with the asymptotic solution of continuum mechanics. The size effect on the failure behaviors of the silicon/carbon nanotube nanocomposites with a fixed longitudinal aspect ratio was further explored, where the carbon nanotube shell was found to influence the brittle-to-ductile transition behavior of silicon nanowires. We show that the mechanical reliability of brittle silicon nanowires can be significantly improved by encapsulating them with carbon nanotubes because the carbon nanotube shell demonstrates high load-bearing capacity under tension. read less C. W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, and J. Mailoa, “Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture,” npj Computational Materials. 2021. link Times cited: 80 C. Liu et al., “Molecular Dynamics Simulation on Cutting Mechanism in the Hybrid Machining Process of Single-Crystal Silicon,” Nanoscale Research Letters. 2021. link Times cited: 14 M. Alam, L. Lymperakis, and J. Neugebauer, “Phase diagram of grain boundary facet and line junctions in silicon,” Physical Review Materials. 2020. link Times cited: 1 Abstract: The presence of facets and line junctions connecting facets … read more Abstract: The presence of facets and line junctions connecting facets on grain boundaries (GBs) has a strong impact on the properties of structural, functional, and optoelectronic materials: They govern the mobility of interfaces, the segregation of impurities, as well the electronic properties. In the present paper, we employ density-functional theory and modiﬁed embedded atom method calculations to systematically investigate the energetics and thermodynamic stability of these defects. As a prototype system, we consider (cid:2) 3 tilt GBs in Si. By analyzing the energetics of different faceted GBs, we derive a diagram that describes and predicts the reconstruction of these extended defects as a function of facet length and boundary inclination angle. The phase diagram sheds light upon the fundamental mechanisms causing GB faceting phenomena. It demonstrates that the properties of faceting are not determined solely by anisotropic GB energies but by a complex interplay between geometry and microstructure, boundary energies as well as long-range strain interactions. read less Y. Zhang et al., “Sintering reaction and microstructure of MAl (M = Ni, Fe, and Mg) nanoparticles through molecular dynamics simulation,” Chinese Physics B. 2020. link Times cited: 4 Y. Dou, Y. Liu, B. Huddleston, Y. Hammi, and M. Horstemeyer, “A molecular dynamics study of effects of crystal orientation, size scale, and strain rate on penetration mechanisms of monocrystalline copper subjected to impact from a nickel penetrator at very high strain rates,” Acta Mechanica. 2020. link Times cited: 6 Y. Dou, Y. Liu, B. Huddleston, Y. Hammi, and M. Horstemeyer, “A molecular dynamics study of effects of crystal orientation, size scale, and strain rate on penetration mechanisms of monocrystalline copper subjected to impact from a nickel penetrator at very high strain rates,” Acta Mechanica. 2020. link Times cited: 0 D. Kumar, S. Goel, N. Gosvami, and J. Jain, “Towards an improved understanding of plasticity, friction and wear mechanisms in precipitate containing AZ91 Mg alloy,” Materialia. 2020. link Times cited: 8 J.-yu Yang, Y. Zhang, Y. Liu, W. Hu, and X. Dai, “A comparative atomic simulation study of the configurations in M-Al (M = Mg, Ni, and Fe) nanoalloys: influence of alloying ability, surface energy, atomic radius, and atomic arrangement,” Journal of Nanoparticle Research. 2020. link Times cited: 3 J. Gu’enol’e et al., “Assessment and optimization of the fast inertial relaxation engine (fire) for energy minimization in atomistic simulations and its implementation in lammps,” Computational Materials Science. 2019. link Times cited: 89 J. Wei et al., “Modified Embedded Atom Method Potential for Modeling the Thermodynamic Properties of High Thermal Conductivity Beryllium Oxide,” ACS Omega. 2019. link Times cited: 8 Abstract: Modified embedded atom method potential parameters of beryll… read more Abstract: Modified embedded atom method potential parameters of beryllium oxide (BeO) have been developed, which can well reproduce the thermodynamic properties of beryllium oxide. To accurately describe the interactions between the atoms in the BeO structure, the density functional theory is used to calculate the fundamental properties such as the lattice constant, bulk modulus, and elastic constant, which are used for the potential fitting. The properties such as the enthalpy and specific heat are used to test the validity of the potential parameters. The calculated results by the developed potential parameters are compared with the experimental and other theoretical data as a function of temperature. The good agreement between the calculated results by the new potential and the experimental data verifies the potential parameters. The developed potential parameters have also been used to predict the thermal conductivity of BeO as a function of temperature for further applications of beryllium oxide. read less H. Wang, X. Guo, L. Zhang, H. Wang, and J. Xue, “Deep learning inter-atomic potential model for accurate irradiation damage simulations,” Applied Physics Letters. 2019. link Times cited: 32 Abstract: We propose a hybrid scheme that interpolates smoothly the Zi… read more Abstract: We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field. read less A. Vaid, J. Gu’enol’e, A. Prakash, S. Korte-Kerzel, and E. Bitzek, “Atomistic simulations of basal dislocations in Mg interacting with Mg17Al12 precipitates,” Materialia. 2019. link Times cited: 29 Y. Lysogorskiy, T. Hammerschmidt, J. Janssen, J. Neugebauer, and R. Drautz, “Transferability of interatomic potentials for molybdenum and silicon,” Modelling and Simulation in Materials Science and Engineering. 2019. link Times cited: 14 Abstract: Interatomic potentials are widely used in computational mate… read more Abstract: Interatomic potentials are widely used in computational materials science, in particular for simulations that are too computationally expensive for density functional theory (DFT). Most interatomic potentials have a limited application range and often there is very limited information available regarding their performance for specific simulations. We carried out high-throughput calculations for molybdenum and silicon with DFT and a number of interatomic potentials. We compare the DFT reference calculations and experimental data to the predictions of the interatomic potentials. We focus on a large number of basic materials properties, including the cohesive energy, atomic volume, elastic coefficients, vibrational properties, thermodynamic properties, surface energies and vacancy formation energies, which enables a detailed discussion of the performance of the different potentials. We further analyze correlations between properties as obtained from DFT calculations and how interatomic potentials reproduce these correlations, and suggest a general measure for quantifying the accuracy and transferability of an interatomic potential. From our analysis we do not establish a clearcut ranking of the potentials as each potential has its strengths and weaknesses. It is therefore essential to assess the properties of a potential carefully before application of the potential in a specific simulation. The data presented here will be useful for selecting a potential for simulations of Mo or Si. read less B. Liu, Y. Li, Y. Yue, Y. Tao, Z. Qin, and C. Cheng, “Molecular Dynamics Investigation on the Phosphorus Doping Effects on the Mechanical Properties of Crystal Silicon,” DEStech Transactions on Engineering and Technology Research. 2019. link Times cited: 0 Abstract: The effects of phosphorus (P), one of the most common impuri… read more Abstract: The effects of phosphorus (P), one of the most common impurities in silicon (Si), on the mechanical responses of crystal Si (c-Si) under tension are investigated using molecular dynamics with a Modified Embedded Atom Method (MEAM) potential. Tensile tests at 300K are applied for bulk c-Si with uniformly distributed and aggregated P impurities, notched c-Si films with P doping on the crack tip or at the middle of the crack propagation path. For bulk c-Si, local defects come into being around P, then rapidly nucleate and propagate, finally lead to brittle fracture. The fracture threshold decreases as the concentration increases, no matter P atoms are uniformly distributed or regionally aggregated. However, for notched c-Si film, P can evidently enhance its fracture strength by blocking the origin and propagation of cracks. With regard to Si-based micro/nano structures, fracture usually starts from the surface, indicating that P impurities play a critical role on the surface. read less L. Zhang, Y. Shibuta, X. Huang, C. Lu, and M. Liu, “Grain boundary induced deformation mechanisms in nanocrystalline Al by molecular dynamics simulation: From interatomic potential perspective,” Computational Materials Science. 2019. link Times cited: 39 G. Almyras, D. Sangiovanni, and K. Sarakinos, “Semi-Empirical Force-Field Model for the Ti1−xAlxN (0 ≤ x ≤ 1) System,” Materials. 2019. link Times cited: 21 Abstract: We present a modified embedded atom method (MEAM) semi-empir… read more Abstract: We present a modified embedded atom method (MEAM) semi-empirical force-field model for the Ti1−xAlxN (0 ≤ x ≤ 1) alloy system. The MEAM parameters, determined via an adaptive simulated-annealing (ASA) minimization scheme, optimize the model’s predictions with respect to 0 K equilibrium volumes, elastic constants, cohesive energies, enthalpies of mixing, and point-defect formation energies, for a set of ≈40 elemental, binary, and ternary Ti-Al-N structures and configurations. Subsequently, the reliability of the model is thoroughly verified against known finite-temperature thermodynamic and kinetic properties of key binary Ti-N and Al-N phases, as well as properties of Ti1−xAlxN (0 < x < 1) alloys. The successful outcome of the validation underscores the transferability of our model, opening the way for large-scale molecular dynamics simulations of, e.g., phase evolution, interfacial processes, and mechanical response in Ti-Al-N-based alloys, superlattices, and nanostructures. read less L. Zhang, D.-Y. Lin, H. Wang, R. Car, and E. Weinan, “Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation,” ArXiv. 2018. link Times cited: 246 Abstract: An active learning procedure called Deep Potential Generator… read more Abstract: An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data. read less J. J. Moller et al., “110 planar faults in strained bcc metals: Origins and implications of a commonly observed artifact of classical potentials,” Physical Review Materials. 2018. link Times cited: 18 Abstract: Large-scale atomistic simulations with classical potentials … read more Abstract: Large-scale atomistic simulations with classical potentials can provide valuable insights into microscopic deformation mechanisms and defect-defect interactions in materials. Unfortunately, these assets often come with the uncertainty of whether the observed mechanisms are based on realistic physical phenomena or whether they are artifacts of the employed material models. One such example is the often reported occurrence of stable planar faults (PFs) in body-centered cubic (bcc) metals subjected to high strains, e.g., at crack tips or in strained nano-objects. In this paper, we study the strain dependence of the generalized stacking fault energy (GSFE) of {110} planes in various bcc metals with material models of increasing sophistication, i.e., (modified) embedded atom method, angular-dependent, Tersoff, and bond-order potentials as well as density functional theory. We show that under applied tensile strains the GSFE curves of many classical potentials exhibit a local minimum which gives rise to the formation of stable PFs. These PFs do not appear when more sophisticated material models are used and have thus to be regarded as artifacts of the potentials. We demonstrate that the local GSFE minimum is not formed for reasons of symmetry and we recommend including the determination of the strain-dependent (110) GSFE as a benchmark for newly developed potentials. read less Y. Xu, F. Zhu, M. Wang, X. Liu, and S. Liu, “Molecular Dynamics Simulation on Grinding Process of Cu-Si and Cu-SiO2 Composite Structures,” 2018 19th International Conference on Electronic Packaging Technology (ICEPT). 2018. link Times cited: 2 Abstract: The molecular dynamics (MD) simulation was performed for Cu-… read more Abstract: The molecular dynamics (MD) simulation was performed for Cu-Si and Cu-SiO2 nano-metric grinding models. The grinding depth and speed were considered to investigate influence of them in grinding. The transformation of the atomic crystal structure of the workpiece during the grinding process was investigated to reveal the material removal mechanism in nano-grinding. The variation of grinding force between the two models was analyzed. The results showed that grinding force was mainly composed of tangential force and normal force. Under the same grinding parameters, the grinding force of the two models changed similarly. read less D. Dickel, C. Barrett, R. Cariño, M. Baskes, and M. Horstemeyer, “Mechanical instabilities in the modeling of phase transitions of titanium,” Modelling and Simulation in Materials Science and Engineering. 2018. link Times cited: 16 Abstract: In this paper, we demonstrate that previously observed β to … read more Abstract: In this paper, we demonstrate that previously observed β to α transitions for titanium interatomic potentials available in the literature arose from a mechanical instability and thus the potentials underestimated the correct thermodynamic phase transition temperature by hundreds of degrees. Using a relative free energy method for the two phases to calculate the true transition temperature, we present a new modified embedded atom method potential for titanium that shows a transition temperature of 1155 ± 2 K in excellent agreement with the experimentally observed transition. This free energy approach avoids the problems of irreversibility which occur when one relies on direct observation of the phase transition in molecular dynamics simulation. Other transformation mechanisms in addition to the mechanical instability are also considered. Finally, the new potential predicts the proper c-axis plastic twinning for titanium under compression making it the only potential that correctly predicts the phase transition temperature and the plastic behavior of α Ti. read less L. Hale, Z. Trautt, and C. Becker, “Evaluating variability with atomistic simulations: the effect of potential and calculation methodology on the modeling of lattice and elastic constants,” Modelling and Simulation in Materials Science and Engineering. 2018. link Times cited: 40 Abstract: Atomistic simulations using classical interatomic potentials… read more Abstract: Atomistic simulations using classical interatomic potentials are powerful investigative tools linking atomic structures to dynamic properties and behaviors. It is well known that different interatomic potentials produce different results, thus making it necessary to characterize potentials based on how they predict basic properties. Doing so makes it possible to compare existing interatomic models in order to select those best suited for specific use cases, and to identify any limitations of the models that may lead to unrealistic responses. While the methods for obtaining many of these properties are often thought of as simple calculations, there are many underlying aspects that can lead to variability in the reported property values. For instance, multiple methods may exist for computing the same property and values may be sensitive to certain simulation parameters. Here, we introduce a new high-throughput computational framework that encodes various simulation methodologies as Python calculation scripts. Three distinct methods for evaluating the lattice and elastic constants of bulk crystal structures are implemented and used to evaluate the properties across 120 interatomic potentials, 18 crystal prototypes, and all possible combinations of unique lattice site and elemental model pairings. Analysis of the results reveals which potentials and crystal prototypes are sensitive to the calculation methods and parameters, and it assists with the verification of potentials, methods, and molecular dynamics software. The results, calculation scripts, and computational infrastructure are self-contained and openly available to support researchers in performing meaningful simulations. read less X. W. Zhou, D. Ward, and M. E. Foster, “A bond-order potential for the Al–Cu–H ternary system,” New Journal of Chemistry. 2018. link Times cited: 13 Abstract: Al-Based Al–Cu alloys have a very high strength to density r… read more Abstract: Al-Based Al–Cu alloys have a very high strength to density ratio, and are therefore important materials for transportation systems including vehicles and aircrafts. These alloys also appear to have a high resistance to hydrogen embrittlement, and as a result, are being explored for hydrogen related applications. To enable fundamental studies of mechanical behavior of Al–Cu alloys under hydrogen environments, we have developed an Al–Cu–H bond-order potential according to the formalism implemented in the molecular dynamics code LAMMPS. Our potential not only fits well to properties of a variety of elemental and compound configurations (with coordination varying from 1 to 12) including small clusters, bulk lattices, defects, and surfaces, but also passes stringent molecular dynamics simulation tests that sample chaotic configurations. Careful studies verified that this Al–Cu–H potential predicts structural property trends close to experimental results and quantum-mechanical calculations; in addition, it properly captures Al–Cu, Al–H, and Cu–H phase diagrams and enables simulations of H2 dissociation, chemisorption, and absorption on Al–Cu surfaces. read less M. Tschopp, B. Rinderspacher, S. Nouranian, M. Baskes, S. Gwaltney, and M. Horstemeyer, “Quantifying Parameter Sensitivity and Uncertainty for Interatomic Potential Design: Application to Saturated Hydrocarbons,” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering. 2018. link Times cited: 8 S. Sun, B. Ramachandran, and C. Wick, “Solid, liquid, and interfacial properties of TiAl alloys: parameterization of a new modified embedded atom method model,” Journal of Physics: Condensed Matter. 2018. link Times cited: 11 Abstract: New interatomic potentials for pure Ti and Al, and binary Ti… read more Abstract: New interatomic potentials for pure Ti and Al, and binary TiAl were developed utilizing the second nearest neighbour modified embedded-atom method (MEAM) formalism. The potentials were parameterized to reproduce multiple properties spanning bulk solids, solid surfaces, solid/liquid phase changes, and liquid interfacial properties. This was carried out using a newly developed optimization procedure that combined the simple minimization of a fitness function with a genetic algorithm to efficiently span the parameter space. The resulting MEAM potentials gave good agreement with experimental and DFT solid and liquid properties, and reproduced the melting points for Ti, Al, and TiAl. However, the surface tensions from the model consistently underestimated experimental values. Liquid TiAl’s surface was found to be mostly covered with Al atoms, showing that Al has a significant propensity for the liquid/air interface. read less M. K. Nahhas and S. Groh, “Atomistic modeling of grain boundary behavior under shear conditions in magnesium and magnesium-based binary alloys,” Journal of Physics and Chemistry of Solids. 2018. link Times cited: 12 R. Marimpul, “Effect of substrate temperature on quality of copper film catalyst substrate: A Molecular Dynamics Study.” 2017. link Times cited: 3 Abstract: Copper film growth using thermal evaporation methods was stu… read more Abstract: Copper film growth using thermal evaporation methods was studied using molecular dynamics simulations. The AlSiMgCuFe modified embedded atom method potential was used to describe interaction of Cu-Cu, Si-Si and Cu-Si atoms. Our results showed that the variations of substrate temperature affected crystal structure composition and surface roughness of the produced copper film catalyst substrate. In this study, we observed intermixing phenomenon after deposition process. The increasing of substrate temperature affected the increasing of the total silicon atoms had diffusion into copper film. read less A. Mahata, M. A. Zaeem, and M. Baskes, “Understanding homogeneous nucleation in solidification of aluminum by molecular dynamics simulations,” Modelling and Simulation in Materials Science and Engineering. 2017. link Times cited: 65 Abstract: Homogeneous nucleation from aluminum (Al) melt was investiga… read more Abstract: Homogeneous nucleation from aluminum (Al) melt was investigated by million-atom molecular dynamics simulations utilizing the second nearest neighbor modified embedded atom method potentials. The natural spontaneous homogenous nucleation from the Al melt was produced without any influence of pressure, free surface effects and impurities. Initially isothermal crystal nucleation from undercooled melt was studied at different constant temperatures, and later superheated Al melt was quenched with different cooling rates. The crystal structure of nuclei, critical nucleus size, critical temperature for homogenous nucleation, induction time, and nucleation rate were determined. The quenching simulations clearly revealed three temperature regimes: sub-critical nucleation, super-critical nucleation, and solid-state grain growth regimes. The main crystalline phase was identified as face-centered cubic, but a hexagonal close-packed (hcp) and an amorphous solid phase were also detected. The hcp phase was created due to the formation of stacking faults during solidification of Al melt. By slowing down the cooling rate, the volume fraction of hcp and amorphous phases decreased. After the box was completely solid, grain growth was simulated and the grain growth exponent was determined for different annealing temperatures. read less C. Angelie and J. Soudan, “Nanothermodynamics of iron clusters: Small clusters, icosahedral and fcc-cuboctahedral structures.,” The Journal of chemical physics. 2017. link Times cited: 3 Abstract: The study of the thermodynamics and structures of iron clust… read more Abstract: The study of the thermodynamics and structures of iron clusters has been carried on, focusing on small clusters and initial icosahedral and fcc-cuboctahedral structures. Two combined tools are used. First, energy intervals are explored by the Monte Carlo algorithm, called σ-mapping, detailed in the work of Soudan et al. [J. Chem. Phys. 135, 144109 (2011), Paper I]. In its flat histogram version, it provides the classical density of states, gp(Ep), in terms of the potential energy of the system. Second, the iron system is described by a potential which is called "corrected EAM" (cEAM), explained in the work of Basire et al. [J. Chem. Phys. 141, 104304 (2014), Paper II]. Small clusters from 3 to 12 atoms in their ground state have been compared first with published Density Functional Theory (DFT) calculations, giving a complete agreement of geometries. The series of 13, 55, 147, and 309 atom icosahedrons is shown to be the most stable form for the cEAM potential. However, the 147 atom cluster has a special behaviour, since decreasing the energy from the liquid zone leads to the irreversible trapping of the cluster in a reproducible amorphous state, 7.38 eV higher in energy than the icosahedron. This behaviour is not observed at the higher size of 309 atoms. The heat capacity of the 55, 147, and 309 atom clusters revealed a pronounced peak in the solid zone, related to a solid-solid transition, prior to the melting peak. The corresponding series of 13, 55, and 147 atom cuboctahedrons has been compared, underscoring the unstability towards the icosahedral structure. This unstability occurs clearly in several steps for the 147 atom cluster, with a sudden transformation at a transition state. This illustrates the concerted icosahedron-cuboctahedron transformation of Buckminster Fuller-Mackay, which is calculated for the cEAM potential. Two other clusters of initial fcc structures with 24 and 38 atoms have been studied, as well as a 302 atom cluster. Each one relaxes towards a more stable structure without regularity. The 38 atom cluster exhibits a nearly glassy relaxation, through a cascade of six metastable states of long life. This behaviour, as that of the 147 atom cluster towards the amorphous state, shows that difficulties to reach ergodicity in the lower half of the solid zone are related to particular features of the potential energy landscape, and not necessarily to a too large size of the system. Comparisons of the cEAM iron system with published results about Lennard-Jones systems and DFT calculations are made. The results of the previous clusters have been combined with that of Paper II to plot the cohesive energy Ec and the melting temperature Tm in terms of the cluster atom number Nat. The Nat-1/3 linear dependence of the melting temperature (Pawlow law) is observed again for Nat > 150. In contrast, for Nat < 150, the curve diverges strongly from the Pawlow law, giving it an overall V-shape, with a linear increase of Tm when Nat goes from 55 to 13 atoms. Surprisingly, the 38 atom cluster is anomalously below the overall curve. read less G. P. P. Pun and Y. Mishin, “Optimized interatomic potential for silicon and its application to thermal stability of silicene,” Physical Review B. 2017. link Times cited: 35 Abstract: An optimized interatomic potential has been constructed for … read more Abstract: An optimized interatomic potential has been constructed for silicon using a modified Tersoff model. The potential reproduces a wide range of properties of Si and improves over existing potentials with respect to point defect structures and energies, surface energies and reconstructions, thermal expansion, melting temperature, and other properties. The proposed potential is compared with three other potentials from the literature. The potentials demonstrate reasonable agreement with first-principles binding energies of small Si clusters as well as single-layer and bilayer silicenes. The four potentials are used to evaluate the thermal stability of free-standing silicenes in the form of nanoribbons, nanoflakes, and nanotubes. While single-layer silicene is found to be mechanically stable at zero Kelvin, it is predicted to become unstable and collapse at room temperature. By contrast, the bilayer silicene demonstrates a larger bending rigidity and remains stable at and even above room temperature. The results suggest that bilayer silicene might exist in a free-standing form at ambient conditions. read less M. Zacate, “Indium-defect interactions in FCC and BCC metals studied using the modified embedded atom method,” Hyperfine Interactions. 2016. link Times cited: 1 X. W. Zhou, D. Ward, and M. E. Foster, “An analytical bond-order potential for the aluminum copper binary system,” Journal of Alloys and Compounds. 2016. link Times cited: 38 S. Groh, “Modified embedded-atom potential for B2-MgAg,” Modelling and Simulation in Materials Science and Engineering. 2016. link Times cited: 5 Abstract: Interatomic potentials for pure Ag and Mg–Ag alloy have been… read more Abstract: Interatomic potentials for pure Ag and Mg–Ag alloy have been developed in the framework of the second nearest-neighbors modified embedded-atom method (MEAM). The validity and the transferability of the Ag potential were obtained by calculating physical, mechanical, thermal, and dislocation related properties. Since the {1 1 1}-generalized stacking fault energy curves obtained from first-principle calculations was used to develop the Ag potential, the critical resolved shear stress to move screw dislocations in Ag single crystal is in good agreement with the experimental data. By combining the ability of the potential to predict the surface energies with its accuracy in describing dislocation properties, the potential is thought to be a predictive model for analyzing the fracture properties of Ag. In addition, the performance of the potential was tested under dynamics conditions by predicting the melting temperature, where a good agreement with experimental value was found. The Ag-MEAM potential was then coupled to an existing Mg-MEAM potential to describe the properties of the binary system MgAg. While the heat of formation, the elastic constants, and the (1 1 0) γ-surface of the MgAg compound in the B2 phase were used to parameterize the potential, heat of formation for MgAg alloys with different stoichiometry, thermal properties of the B2-MgAg compound, as well as dislocation related properties in B2-MgAg compound were tested to validate the transferability of the potential. The heat of formation of Mg5Ag2, MgAg, and MgAg3, the elastic constants and the thermal properties of B2-MgAg obtained with the proposed potential align with first-principles and experimental data. In addition, the core structure of both 〈0 0 1〉 and 〈1 1 1〉 dislocations in {1 1 0} are in agreement with theoretical predictions, and the magnitudes of the critical resolved shear stress obtained at 0 K for both slip systems partially validate the slip behavior of B2-MgAg. Furthermore, the interaction between silver solute element and dislocations from the basal plane is correctly captured by the potential. read less C. Barrett and R. Cariño, “The MEAM parameter calibration tool: an explicit methodology for hierarchical bridging between ab initio and atomistic scales,” Integrating Materials and Manufacturing Innovation. 2016. link Times cited: 8 K. Wang, W. Zhu, S. Xiao, J. Chen, and W. Hu, “A new embedded-atom method approach based on the pth moment approximation,” Journal of Physics: Condensed Matter. 2016. link Times cited: 5 Abstract: Large scale atomistic simulations with suitable interatomic … read more Abstract: Large scale atomistic simulations with suitable interatomic potentials are widely employed by scientists or engineers of different areas. The quick generation of high-quality interatomic potentials is urgently needed. This largely relies on the developments of potential construction methods and algorithms in this area. Quantities of interatomic potential models have been proposed and parameterized with various methods, such as the analytic method, the force-matching approach and multi-object optimization method, in order to make the potentials more transferable. Without apparently lowering the precision for describing the target system, potentials of fewer fitting parameters (FPs) are somewhat more physically reasonable. Thus, studying methods to reduce the FP number is helpful in understanding the underlying physics of simulated systems and improving the precision of potential models. In this work, we propose an embedded-atom method (EAM) potential model consisting of a new manybody term based on the pth moment approximation to the tight binding theory and the general transformation invariance of EAM potentials, and an energy modification term represented by pairwise interactions. The pairwise interactions are evaluated by an analytic-numerical scheme without the need to know their functional forms a priori. By constructing three potentials of aluminum and comparing them with a commonly used EAM potential model, several wonderful results are obtained. First, without losing the precision of potentials, our potential of aluminum has fewer potential parameters and a smaller cutoff distance when compared with some constantly-used potentials of aluminum. This is because several physical quantities, usually serving as target quantities to match in other potentials, seem to be uniquely dependent on quantities contained in our basic reference database within the new potential model. Second, a key empirical parameter in the embedding term of the commonly used EAM model is found to be related to the effective order of moments of local density of states. This may provide a way to improve the precision of EAM potentials further through more precise approximations to tight binding theory. In addition, some critical details about construction procedures are discussed. read less E. Asadi and M. A. Zaeem, “The anisotropy of hexagonal close-packed and liquid interface free energy using molecular dynamics simulations based on modified embedded-atom method,” Acta Materialia. 2016. link Times cited: 37 S. Parviainen, F. Djurabekova, S. Fitzgerald, A. Ruzibaev, and K. Nordlund, “Atomistic simulations of field assisted evaporation in atom probe tomography,” Journal of Physics D: Applied Physics. 2016. link Times cited: 19 Abstract: Atom probe tomography (APT) is an extremely powerful techniq… read more Abstract: Atom probe tomography (APT) is an extremely powerful technique for determining the three-dimensional structure and chemical composition of a given sample. Although it is designed to provide images of material structure with atomic scale resolution, reconstruction artifacts, well-known to be present in reconstructed images, reduce their accuracy. No existing simulation technique has been able to describe the origin of these artifacts. Here we develop a simulation technique which allows for atomistic simulations of the atom emission process in the presence of high electric fields in APT experiments. Our code combines hybrid concurrent electrodynamics—molecular dynamics and a Monte Carlo approach. We use this technique to demonstrate the atom-level origin of artifacts in APT image reconstructions on examples of inclusions and voids in investigated samples. The results show that even small variations in the surface topology give rise to distortions in the local electric field, limiting the accuracy of conventional APT reconstruction algorithms. read less Z. Wu, M. Francis, and W. Curtin, “Magnesium interatomic potential for simulating plasticity and fracture phenomena,” Modelling and Simulation in Materials Science and Engineering. 2015. link Times cited: 128 Abstract: Magnesium has multiple dislocation and twinning systems with… read more Abstract: Magnesium has multiple dislocation and twinning systems with starkly different properties, which make its plastic deformation strongly anisotropic and highly complex. Existing empirical interatomic potentials fail to capture the full scope of these properties, making current molecular statics and dynamics simulation results of limited quantitative and predictive use. Here, based on the work by Kim et al, a new modified embedded-atom method potential for magnesium is introduced and rigorously validated against existing ab initio, continuum theory and experimental results. The new potential satisfactorily reproduces all the necessary mechanical properties for plastic deformation, including the various generalized stacking fault energy surfaces, dislocations core structures, Peierls stresses, surface energies and basal plane cohesive strength. The capability of this potential to accurately describe all the important slip systems and fracture behavior makes it valuable for future realistic atomistic studies of general magnesium deformation and failure problems. read less C. P. Chui, W. Liu, Y. Xu, and Y. Zhou, “Molecular Dynamics Simulation of Iron — A Review.” 2015. link Times cited: 3 Abstract: Molecular dynamics (MD) is a technique of atomistic simulati… read more Abstract: Molecular dynamics (MD) is a technique of atomistic simulation which has facilitated scientific discovery of interactions among particles since its advent in the late 1950s. Its merit lies in incorporating statistical mechanics to allow for examination of varying atomic configurations at finite temperatures. Its contributions to materials science from modeling pure metal properties to designing nanowires is also remarkable. This review paper focuses on the progress of MD in understanding the behavior of iron — in pure metal form, in alloys, and in composite nanomaterials. It also discusses the interatomic potentials and the integration algorithms used for simulating iron in the literature. Furthermore, it reveals the current progress of MD in simulating iron by exhibiting some results in the literature. Finally, the review paper briefly mentions the development of the hardware and software tools for such large-scale computations. read less A. Goryaeva, P. Carrez, and P. Cordier, “Modeling defects and plasticity in MgSiO3 post-perovskite: Part 1—generalized stacking faults,” Physics and Chemistry of Minerals. 2015. link Times cited: 14 S. Groh and M. Alam, “Fracture behavior of lithium single crystal in the framework of (semi-)empirical force field derived from first-principles,” Modelling and Simulation in Materials Science and Engineering. 2015. link Times cited: 11 Abstract: An approach to derive, from first-principles data, accurate … read more Abstract: An approach to derive, from first-principles data, accurate and reliable potentials in the modified embedded-atom method in view of modeling the mechanical behavior of metals is presented in this work and applied to the optimization of a potential representative of lithium (Li). Although the theoretical background of the modified embedded-atom method was considered in this work, the proposed method is general and it can be applied to any other functional form. The main feature of the method is to introduce several path transformations in the material database that are critical for plastic and failure behavior. As part of the potential validation, path transformations different from the ones used for the parameterization procedure are considered. Applied in the case of Li, the material database was enriched with the generalized stacking fault energy curve along the <1 1 1> -direction on the {1 1 0}-plane, and with the traction-separation behavior of a {1 0 0}-surface. The path transformations used to enrich the material database were initially derived from first-principles calculations. For validation, the generalized stacking fault energy curves along the <1 1 1> -direction on the {1 1 2}- and {1 2 3}-planes were considered for plasticity, while traction-separation behavior of {1 1 0} and {1 1 1}-planes were considered for failure behavior. As part of the validation procedure, the predictions made in the MEAM framework were validated by first-principles data. The final potential accurately reproduced basic equilibrium properties, elastic constants, surface energies in agreement with first-principles predictions, and transition energy between different crystal structures. Furthermore, generalized stacking fault energy curves along the <1 1 1> -direction on the {1 1 0}, {1 1 2}, and {1 2 3}-planes, and tensile cohesive stress, characteristic length of fracture, and work of separation of a {1 0 0}, {1 1 0}, and {1 1 1} surfaces obtained in the MEAM framework compared well with first-principles predictions. It also predicts good elastic constants for a crystal structure different than the one used for the fitting of the potential and the other four path transformations. The potential was tested for failure behavior using a full atomistic setup, and in addition of being qualitatively correct, the stress intensity factor for different crack orientations was found to be in agreement with the theory of Rice (1992 J. Mech. Phys. Solids 40 239–71) within an error of 10%. Finally, the optimized Li-MEAM potential is expected to be transferable to different local environments encountered in atomistic simulations of lattice defects. read less X. W. Zhou, D. Ward, M. Foster, and J. Zimmerman, “An analytical bond-order potential for the copper–hydrogen binary system,” Journal of Materials Science. 2015. link Times cited: 18 K. Choudhary et al., “Charge optimized many-body potential for aluminum,” Journal of Physics: Condensed Matter. 2014. link Times cited: 19 Abstract: An interatomic potential for Al is developed within the thir… read more Abstract: An interatomic potential for Al is developed within the third generation of the charge optimized many-body (COMB3) formalism. The database used for the parameterization of the potential consists of experimental data and the results of first-principles and quantum chemical calculations. The potential exhibits reasonable agreement with cohesive energy, lattice parameters, elastic constants, bulk and shear modulus, surface energies, stacking fault energies, point defect formation energies, and the phase order of metallic Al from experiments and density functional theory. In addition, the predicted phonon dispersion is in good agreement with the experimental data and first-principles calculations. Importantly for the prediction of the mechanical behavior, the unstable stacking fault energetics along the direction on the (1 1 1) plane are similar to those obtained from first-principles calculations. The polycrsytal when strained shows responses that are physical and the overall behavior is consistent with experimental observations. read less M. Basire, J. Soudan, and C. Angelie, “Nanothermodynamics of large iron clusters by means of a flat histogram Monte Carlo method.,” The Journal of chemical physics. 2014. link Times cited: 2 Abstract: The thermodynamics of iron clusters of various sizes, from 7… read more Abstract: The thermodynamics of iron clusters of various sizes, from 76 to 2452 atoms, typical of the catalyst particles used for carbon nanotubes growth, has been explored by a flat histogram Monte Carlo (MC) algorithm (called the σ-mapping), developed by Soudan et al. [J. Chem. Phys. 135, 144109 (2011), Paper I]. This method provides the classical density of states, gp(Ep) in the configurational space, in terms of the potential energy of the system, with good and well controlled convergence properties, particularly in the melting phase transition zone which is of interest in this work. To describe the system, an iron potential has been implemented, called "corrected EAM" (cEAM), which approximates the MEAM potential of Lee et al. [Phys. Rev. B 64, 184102 (2001)] with an accuracy better than 3 meV/at, and a five times larger computational speed. The main simplification concerns the angular dependence of the potential, with a small impact on accuracy, while the screening coefficients S(ij) are exactly computed with a fast algorithm. With this potential, ergodic explorations of the clusters can be performed efficiently in a reasonable computing time, at least in the upper half of the solid zone and above. Problems of ergodicity exist in the lower half of the solid zone but routes to overcome them are discussed. The solid-liquid (melting) phase transition temperature T(m) is plotted in terms of the cluster atom number N(at). The standard N(at)(-1/3) linear dependence (Pawlow law) is observed for N(at) >300, allowing an extrapolation up to the bulk metal at 1940 ±50 K. For N(at) <150, a strong divergence is observed compared to the Pawlow law. The melting transition, which begins at the surface, is stated by a Lindemann-Berry index and an atomic density analysis. Several new features are obtained for the thermodynamics of cEAM clusters, compared to the Rydberg pair potential clusters studied in Paper I. read less H. Gao, A. Otero-de-la-Roza, S. Aouadi, E. Johnson, and A. Martini, “An empirical model for silver tantalate,” Modelling and Simulation in Materials Science and Engineering. 2013. link Times cited: 13 Abstract: A set of parameters for the modified embedded atom method (M… read more Abstract: A set of parameters for the modified embedded atom method (MEAM) potential was developed to describe the perovskite silver tantalate (AgTaO3). First, MEAM parameters for AgO and TaO were determined based on the structural and elastic properties of the materials in a B1 reference structure predicted by density-functional theory (DFT). Then, using the fitted binary parameters, additional potential parameters were adjusted to enable the empirical potential to reproduce DFT-predicted lattice structure, elastic constants, cohesive energy and equation of state for the ternary AgTaO3. Finally, thermal expansion was predicted by a molecular dynamics (MD) simulation using the newly developed potential and compared directly to experimental values. The agreement with known experimental data for AgTaO3 is satisfactory, and confirms that the new empirical model is a good starting point for further MD studies. read less S. Nouranian, M. Tschopp, M. Tschopp, S. Gwaltney, M. Baskes, and M. Horstemeyer, “An interatomic potential for saturated hydrocarbons based on the modified embedded-atom method.,” Physical chemistry chemical physics : PCCP. 2013. link Times cited: 39 Abstract: In this work, we developed an interatomic potential for satu… read more Abstract: In this work, we developed an interatomic potential for saturated hydrocarbons using the modified embedded-atom method (MEAM), a reactive semi-empirical many-body potential based on density functional theory and pair potentials. We parameterized the potential by fitting to a large experimental and first-principles (FP) database consisting of (1) bond distances, bond angles, and atomization energies at 0 K of a homologous series of alkanes and their select isomers from methane to n-octane, (2) the potential energy curves of H2, CH, and C2 diatomics, (3) the potential energy curves of hydrogen, methane, ethane, and propane dimers, i.e., (H2)2, (CH4)2, (C2H6)2, and (C3H8)2, respectively, and (4) pressure-volume-temperature (PVT) data of a dense high-pressure methane system with the density of 0.5534 g cc(-1). We compared the atomization energies and geometries of a range of linear alkanes, cycloalkanes, and free radicals calculated from the MEAM potential to those calculated by other commonly used reactive potentials for hydrocarbons, i.e., second-generation reactive empirical bond order (REBO) and reactive force field (ReaxFF). MEAM reproduced the experimental and/or FP data with accuracy comparable to or better than REBO or ReaxFF. The experimental PVT data for a relatively large series of methane, ethane, propane, and butane systems with different densities were predicted reasonably well by the MEAM potential. Although the MEAM formalism has been applied to atomic systems with predominantly metallic bonding in the past, the current work demonstrates the promising extension of the MEAM potential to covalently bonded molecular systems, specifically saturated hydrocarbons and saturated hydrocarbon-based polymers. The MEAM potential has already been parameterized for a large number of metallic unary, binary, ternary, carbide, nitride, and hydride systems, and extending it to saturated hydrocarbons provides a reliable and transferable potential for atomistic/molecular studies of complex material phenomena involving hydrocarbon-metal or polymer-metal interfaces, polymer-metal nanocomposites, fracture and failure in hydrocarbon-based polymers, etc. The latter is especially true since MEAM is a reactive potential that allows for dynamic bond formation and bond breaking during simulation. Our results show that MEAM predicts the energetics of two major chemical reactions for saturated hydrocarbons, i.e., breaking a C-C and a C-H bond, reasonably well. However, the current parameterization does not accurately reproduce the energetics and structures of unsaturated hydrocarbons and, therefore, should not be applied to such systems. read less H. Zhou, D. Dickel, M. Baskes, S. Mun, and M. A. Zaeem, “A modified embedded-atom method interatomic potential for bismuth,” Modelling and Simulation in Materials Science and Engineering. 2021. link Times cited: 5 Abstract: A semi-empirical interatomic potential for the post-transiti… read more Abstract: A semi-empirical interatomic potential for the post-transition metal, bismuth, is developed based on the second nearest-neighbor modified embedded-atom method (MEAM). The potential reproduces a range of physical properties, such as the lattice constant, cohesive energy, elastic constants, vacancy formation energy, surface energy, and the melting point of pure bismuth. The calculations are done for the rhombohedral ground state of Bi. The results show good agreement with density functional theory and experimental data. The developed MEAM potential for bismuth is useful for material and mechanical behavior studies of the pure material at different conditions and sets the stage for the development of interatomic potentials for bismuth alloys or other bismuth compounds. read less N. Razmara and R. Mohammadzadeh, “Molecular dynamics study of nitrogen diffusion in nanocrystalline iron,” Journal of Molecular Modeling. 2016. link Times cited: 6 E. Asadi and M. A. Zaeem, “A Review of Quantitative Phase-Field Crystal Modeling of Solid–Liquid Structures,” JOM. 2015. link Times cited: 44
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_262519520678_002
Extended KIM ID The long form of the KIM ID including a human readable prefix (100 characters max), two underscores, and the Short KIM ID. Extended KIM IDs can only contain alpha-numeric characters (letters and digits) and underscores and must begin with a letter.	MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_002
DOI	10.25950/8d75422b https://doi.org/10.25950/8d75422b https://commons.datacite.org/doi.org/10.25950/8d75422b
KIM Item Type Specifies whether this is a Portable Model (software implementation of an interatomic model); Portable Model with parameter file (parameter file to be read in by a Model Driver); Model Driver (software implementation of an interatomic model that reads in parameters).	Portable Model using Model Driver MEAM_LAMMPS__MD_249792265679_002
Driver	MEAM_LAMMPS__MD_249792265679_002
KIM API Version	2.2
Potential Type	meam

Previous Version	MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_001

Verification Check Dashboard

(Click here to learn more about Verification Checks)

Grade	Name	Category	Brief Description	Full Results	Aux File(s)
P All elements claimed by model are supported in at least one of the tested configurations.	vc-species-supported-as-stated	mandatory	The model supports all species it claims to support; see full description.	Results	Files
P Periodic boundary conditions were correctly supported for all configurations that the model was able to compute.	vc-periodicity-support	mandatory	Periodic boundary conditions are handled correctly; see full description.	Results	Files
P Model energy has permutation symmetry for all configurations the model was able to compute.	vc-permutation-symmetry	mandatory	Total energy and forces are unchanged when swapping atoms of the same species; see full description.	Results	Files
B The letter grade B was assigned because the normalized error in the computation was 8.35358e-09 compared with a machine precision of 2.22045e-16. The letter grade was based on 'score=log10(error/eps)', with ranges A=[0, 7.5], B=(7.5, 10.0], C=(10.0, 12.5], D=(12.5, 15.0), F>15.0. 'A' is the best grade, and 'F' indicates failure.	vc-forces-numerical-derivative	consistency	Forces computed by the model agree with numerical derivatives of the energy; see full description.	Results	Files
F The model is C^-1 continuous. This means that the model has discontinuous energy.	vc-dimer-continuity-c1	informational	The energy versus separation relation of a pair of atoms is C1 continuous (i.e. the function and its first derivative are continuous); see full description.	Results	Files
P Model energy and forces are invariant with respect to rigid-body motion (translation and rotation) for all configurations the model was able to compute.	vc-objectivity	informational	Total energy is unchanged and forces transform correctly under rigid-body translation and rotation; see full description.	Results	Files
P Model energy has inversion symmetry for all configurations the model was able to compute.	vc-inversion-symmetry	informational	Total energy is unchanged and forces change sign when inverting a configuration through the origin; see full description.	Results	Files
P No memory leak detected.	vc-memory-leak	informational	The model code does not have memory leaks (i.e. it releases all allocated memory at the end); see full description.	Results	Files
P All threads give identical results for tested case. Model appears to be thread-safe.	vc-thread-safe	mandatory	The model returns the same energy and forces when computed in serial and when using parallel threads for a set of configurations. Note that this is not a guarantee of thread safety; see full description.	Results	Files
P Unit conversion successful	vc-unit-conversion	mandatory	The model is able to correctly convert its energy and/or forces to different unit sets; see full description.	Results	Files

This bar chart plot shows the mono-atomic body-centered cubic (bcc) lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Mg

Species: Cu

Species: Si

Species: Al

Species: Fe

Cohesive Energy Graph

This graph shows the cohesive energy versus volume-per-atom for the current mode for four mono-atomic cubic phases (body-centered cubic (bcc), face-centered cubic (fcc), simple cubic (sc), and diamond). The curve with the lowest minimum is the ground state of the crystal if stable. (The crystal structure is enforced in these calculations, so the phase may not be stable.) Graphs are generated for each species supported by the model.

Species: Cu

Species: Mg

Species: Fe

Species: Al

Species: Si

Diamond Lattice Constant

This bar chart plot shows the mono-atomic face-centered diamond lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Cu

Species: Si

Species: Al

Species: Fe

Species: Mg

Dislocation Core Energies

This graph shows the dislocation core energy of a cubic crystal at zero temperature and pressure for a specific set of dislocation core cutoff radii. After obtaining the total energy of the system from conjugate gradient minimizations, non-singular, isotropic and anisotropic elasticity are applied to obtain the dislocation core energy for each of these supercells with different dipole distances. Graphs are generated for each species supported by the model.

(No matching species)

FCC Elastic Constants

This bar chart plot shows the mono-atomic face-centered cubic (fcc) elastic constants predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Cu

Species: Fe

Species: Si

Species: Al

Species: Mg

FCC Lattice Constant

This bar chart plot shows the mono-atomic face-centered cubic (fcc) lattice constant predicted by the current model (shown in red) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Al

Species: Mg

Species: Cu

Species: Fe

Species: Si

FCC Stacking Fault Energies

This bar chart plot shows the intrinsic and extrinsic stacking fault energies as well as the unstable stacking and unstable twinning energies for face-centered cubic (fcc) predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Cu

Species: Al

FCC Surface Energies

This bar chart plot shows the mono-atomic face-centered cubic (fcc) relaxed surface energies predicted by the current model (shown in blue) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Al

Species: Cu

SC Lattice Constant

This bar chart plot shows the mono-atomic simple cubic (sc) lattice constant predicted by the current model (shown in the unique color) compared with the predictions for all other models in the OpenKIM Repository that support the species. The vertical bars show the average and standard deviation (one sigma) bounds for all model predictions. Graphs are generated for each species supported by the model.

Species: Si

Species: Fe

Species: Mg

Species: Cu

Species: Al

Cubic Crystal Basic Properties Table

Species: Al

Species: Cu

Species: Fe

Species: Mg

Species: Si

Tests

ClusterEnergyAndForces__TD_000043093022_003

Conjugate gradient relaxation of atomic cluster v003

Creators: Daniel S. Karls
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/b47dd4c4

Given an xyz file corresponding to a finite cluster of atoms, this Test Driver computes the total potential energy and atomic forces on the configuration. The positions are then relaxed using conjugate gradient minimization and the final positions and forces are recorded. These results are primarily of interest for training machine-learning algorithms.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
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Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4506
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4638
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4704
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7509
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4785
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10454
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2798
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9129
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4923
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4287
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9718
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3166
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9792
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4784
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3018
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8098
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4466
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4197
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3681
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4227
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10012
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5301
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9644
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4714
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11927
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4943
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4307
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3387
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2871
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6626
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8172
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4535
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4638
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4764
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5374
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8834
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6258
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11411
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13178
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6331
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13325
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4854
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4297
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3092
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5890
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7141
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4913
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6994
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12589
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4874
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8761
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4515
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4297
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4237
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5227
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4704
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3387
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4446
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5301
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4774
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5082
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11043
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8245
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5669
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4406
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6037
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6479
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7436
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4396
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7509
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4734
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4605
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4416
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4049
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7804
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9571
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4555
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5080
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8245
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3092
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12074
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4515
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8319
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11927
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4774
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6699
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5374
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4565
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4714
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6773
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4923
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4794
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3239
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4704
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4270
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4814
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4712
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7215
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3313
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4386
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5522
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4565
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5816
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4814
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4565
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4565
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2945
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8687
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4128
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5890
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6773
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8098
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4535
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12589
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9571
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5006
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4834
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5742
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4923
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3387
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4983
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4933
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3607
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4196
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4406
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4227
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3681
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4336
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4227
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4307
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4943
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12736
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4506
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4859
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4336
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5232
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4555
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6184
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4638
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4327
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9644
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4366
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7509
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9350
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12810
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4585
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3387
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4515
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4874
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4665
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11190
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8393
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11706
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4784
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4396
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10233
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6847
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6994
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4416
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3018
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5301
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6037
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4555
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4844
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4665
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4785
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11927
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3166
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13104
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4466
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4525
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7951
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8540
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4844
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4196
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5080
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4267
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4535
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4466
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4196
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4585
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5153
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3018
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4983
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4764
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4366
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3313
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9939
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9792
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9203
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7288
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9423
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9350
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3166
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11043
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4712
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4903
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6479
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4983
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4386
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6037
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4635
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7288
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4638
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2650
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4506
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12221
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4344
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10086
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4525
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12515
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9055
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6773
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4754
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4933
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11779
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4774
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5522
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12442
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8908
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4466
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4712
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10749
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8614
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5448
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11485
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5816
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4774
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4196
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4585
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4336
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4704
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6920
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2871
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4575
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4605
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4859
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9792
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4864
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3755
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6331
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6626
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4764
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4785
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4336
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10307
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12000
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4386
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4297
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3681
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2577
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5006
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12074
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3828
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4386
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9055
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3681
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5153
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6699
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9497
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4638
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4883
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3607
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4635
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4506
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9423
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9423
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4336
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4784
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12736
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12736
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11853
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4714
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4436
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4804
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5669
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4785
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4436
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4417
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6258
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4466
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5033
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12295
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4695
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4704
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7215
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8982
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5281
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5890
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12589
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5742
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4396
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11411
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3387
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4307
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2429
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3976
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4277
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8982
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6847
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4714
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12884
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4525
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3018
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3092
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7583
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2945
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2577
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3534
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5595
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6699
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8393
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3018
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4535
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4327
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4344
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4585
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6184
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4564
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4903
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4605
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4491
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4785
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4416
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8245
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6994
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4794
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4344
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6552
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6847
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11853
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4615
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5301
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4270
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3092
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6405
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6331
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4625
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4859
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4635
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5963
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4545
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4506
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5227
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5080
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4635
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5033
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4724
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5153
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	8319
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6331
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4317
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11853
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4346
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4396
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4436
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5816
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4744
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4665
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2945
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4396
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3239
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4366
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5522
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4257
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4417
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4605
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4933
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13252
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4496
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4237
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2356
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4754
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12221
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13473
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6920
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4515
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2798
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4446
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2650
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4824
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6405
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	9718
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3239
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4196
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4754
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4426
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	11853
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13399
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4675
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	13620
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4307
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4257
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	6037
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4864
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3166
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4645
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3313
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4417
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4456
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4376
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4270
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4247
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4665
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	2798
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	7215
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4655
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	10896
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4476
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4123
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	12663
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4486
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3902
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	5301
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4635
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4685
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4535
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	3460
Conjugate gradient relaxation of random finite cluster of Si atoms v003	view	4356

CohesiveEnergyVsLatticeConstant__TD_554653289799_003

Cohesive energy versus lattice constant curve for monoatomic cubic lattices v003

Creators:
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/64cb38c5

This Test Driver uses LAMMPS to compute the cohesive energy of a given monoatomic cubic lattice (fcc, bcc, sc, or diamond) at a variety of lattice spacings. The lattice spacings range from a_min (=a_min_frac*a_0) to a_max (=a_max_frac*a_0) where a_0, a_min_frac, and a_max_frac are read from stdin (a_0 is typically approximately equal to the equilibrium lattice constant). The precise scaling and number of lattice spacings sampled between a_min and a_0 (a_0 and a_max) is specified by two additional parameters passed from stdin: N_lower and samplespacing_lower (N_upper and samplespacing_upper). Please see README.txt for further details.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Cohesive energy versus lattice constant curve for bcc Al v004	view	24984
Cohesive energy versus lattice constant curve for bcc Cu v004	view	25293
Cohesive energy versus lattice constant curve for bcc Fe v004	view	24517
Cohesive energy versus lattice constant curve for bcc Mg v004	view	28565
Cohesive energy versus lattice constant curve for bcc Si v004	view	25621
Cohesive energy versus lattice constant curve for diamond Al v004	view	28418
Cohesive energy versus lattice constant curve for diamond Cu v004	view	28786
Cohesive energy versus lattice constant curve for diamond Fe v004	view	34443
Cohesive energy versus lattice constant curve for diamond Mg v004	view	29080
Cohesive energy versus lattice constant curve for diamond Si v004	view	24666
Cohesive energy versus lattice constant curve for fcc Al v004	view	25332
Cohesive energy versus lattice constant curve for fcc Cu v004	view	28786
Cohesive energy versus lattice constant curve for fcc Fe v004	view	24228
Cohesive energy versus lattice constant curve for fcc Mg v004	view	29080
Cohesive energy versus lattice constant curve for fcc Si v004	view	28123
Cohesive energy versus lattice constant curve for sc Al v004	view	24537
Cohesive energy versus lattice constant curve for sc Cu v004	view	22747
Cohesive energy versus lattice constant curve for sc Fe v004	view	23731
Cohesive energy versus lattice constant curve for sc Mg v004	view	25313
Cohesive energy versus lattice constant curve for sc Si v004	view	26168

ElasticConstantsCrystal__TD_034002468289_000

Elastic constants for arbitrary crystals at zero temperature and pressure v000

Creators:
Contributor: ilia
Publication Year: 2024
DOI: https://doi.org/10.25950/888f9943

Computes the elastic constants for an arbitrary crystal. A robust computational protocol is used, attempting multiple methods and step sizes to achieve an acceptably low error in numerical differentiation and deviation from material symmetry. The crystal structure is specified using the AFLOW prototype designation as part of the Crystal Genome testing framework. In addition, the distance from the obtained elasticity tensor to the nearest isotropic tensor is computed.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Elastic constants for FeSi in AFLOW crystal prototype A11B5_cP16_221_cg_abd at zero temperature and pressure v000	view	1397391
Elastic constants for AlMg in AFLOW crystal prototype A12B17_cI58_217_g_acg at zero temperature and pressure v000	view	3627724
Elastic constants for AlMg in AFLOW crystal prototype A14B13_cI54_229_ef_ah at zero temperature and pressure v000	view	6341375
Elastic constants for CuSi in AFLOW crystal prototype A15B4_cI76_220_ae_c at zero temperature and pressure v000	view	88127640
Elastic constants for CuMgSi in AFLOW crystal prototype A16B6C7_cF116_225_2f_e_ad at zero temperature and pressure v000	view	2115967
Elastic constants for AlFeSi in AFLOW crystal prototype A2B3C3_aP16_2_2i_3i_3i at zero temperature and pressure v000	view	4150151
Elastic constants for AlFeSi in AFLOW crystal prototype A2B3C4_oC36_63_f_cf_2cf at zero temperature and pressure v000	view	4008033
Elastic constants for MgSi in AFLOW crystal prototype A2B_cF12_225_c_a at zero temperature and pressure v000	view	372033
Elastic constants for CuMg in AFLOW crystal prototype A2B_cF24_227_c_b at zero temperature and pressure v000	view	856354
Elastic constants for MgSi in AFLOW crystal prototype A2B_cF24_227_c_b at zero temperature and pressure v000	view	498232
Elastic constants for FeSi in AFLOW crystal prototype A2B_hP6_164_abd_d at zero temperature and pressure v000	view	1783604
Elastic constants for MgSi in AFLOW crystal prototype A2B_hP6_194_ac_d at zero temperature and pressure v000	view	355142
Elastic constants for AlMgSi in AFLOW crystal prototype A2BC2_hP5_164_d_a_d at zero temperature and pressure v000	view	2381427
Elastic constants for AlCuMg in AFLOW crystal prototype A2BC_oC16_63_f_c_c at zero temperature and pressure v000	view	1457389

ElasticConstantsCubic__TD_011862047401_006

Elastic constants for cubic crystals at zero temperature and pressure v006

Creators: Junhao Li and Ellad Tadmor
Contributor: tadmor
Publication Year: 2019
DOI: https://doi.org/10.25950/5853fb8f

Computes the cubic elastic constants for some common crystal types (fcc, bcc, sc, diamond) by calculating the hessian of the energy density with respect to strain. An estimate of the error associated with the numerical differentiation performed is reported.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Elastic constants for bcc Al at zero temperature v006	view	55757
Elastic constants for bcc Cu at zero temperature v006	view	67255
Elastic constants for bcc Fe at zero temperature v006	view	62872
Elastic constants for bcc Si at zero temperature v006	view	68393
Elastic constants for diamond Al at zero temperature v001	view	330924
Elastic constants for diamond Cu at zero temperature v001	view	120222
Elastic constants for diamond Fe at zero temperature v001	view	156812
Elastic constants for diamond Mg at zero temperature v001	view	587639
Elastic constants for diamond Si at zero temperature v001	view	215898
Elastic constants for fcc Al at zero temperature v006	view	67289
Elastic constants for fcc Cu at zero temperature v006	view	65596
Elastic constants for fcc Fe at zero temperature v006	view	75629
Elastic constants for fcc Mg at zero temperature v006	view	74209
Elastic constants for fcc Si at zero temperature v006	view	56851
Elastic constants for sc Al at zero temperature v006	view	72443
Elastic constants for sc Cu at zero temperature v006	view	78921
Elastic constants for sc Fe at zero temperature v006	view	62283
Elastic constants for sc Mg at zero temperature v006	view	70050
Elastic constants for sc Si at zero temperature v006	view	66627

EquilibriumCrystalStructure__TD_457028483760_001

Equilibrium structure and energy for a crystal structure at zero temperature and pressure v001

Creators:
Contributor: ilia
Publication Year: 2023
DOI: https://doi.org/10.25950/e8a7ed84

Computes the equilibrium crystal structure and energy for an arbitrary crystal at zero temperature and applied stress by performing symmetry-constrained relaxation. The crystal structure is specified using the AFLOW prototype designation. Multiple sets of free parameters corresponding to the crystal prototype may be specified as initial guesses for structure optimization. No guarantee is made regarding the stability of computed equilibria, nor that any are the ground state.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium crystal structure and energy for Cu in AFLOW crystal prototype A_cF4_225_a v001		view	86799
Equilibrium crystal structure and energy for Cu in AFLOW crystal prototype A_cI2_229_a v001		view	76197

EquilibriumCrystalStructure__TD_457028483760_002

Equilibrium structure and energy for a crystal structure at zero temperature and pressure v002

Creators:
Contributor: ilia
Publication Year: 2024
DOI: https://doi.org/10.25950/2f2c4ad3

Computes the equilibrium crystal structure and energy for an arbitrary crystal at zero temperature and applied stress by performing symmetry-constrained relaxation. The crystal structure is specified using the AFLOW prototype designation. Multiple sets of free parameters corresponding to the crystal prototype may be specified as initial guesses for structure optimization. No guarantee is made regarding the stability of computed equilibria, nor that any are the ground state.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A11B5_cP16_221_cg_abd v002	view	81904
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A12B17_cI58_217_g_acg v002	view	212469
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A14B13_cI54_229_ef_ah v002	view	190604
Equilibrium crystal structure and energy for CuSi in AFLOW crystal prototype A15B4_cI76_220_ae_c v002	view	284911
Equilibrium crystal structure and energy for CuMgSi in AFLOW crystal prototype A16B6C7_cF116_225_2f_e_ad v001	view	884058
Equilibrium crystal structure and energy for AlFeSi in AFLOW crystal prototype A2B3C3_aP16_2_2i_3i_3i v001	view	65560
Equilibrium crystal structure and energy for AlFeSi in AFLOW crystal prototype A2B3C4_oC36_63_f_cf_2cf v001	view	265476
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A2B_cF12_225_c_a v002	view	148566
Equilibrium crystal structure and energy for CuMg in AFLOW crystal prototype A2B_cF24_227_c_b v002	view	261868
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A2B_cF24_227_c_b v002	view	275562
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A2B_hP6_164_abd_d v002	view	61975
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A2B_hP6_194_ac_d v002	view	58087
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A2B_oP12_62_2c_c v002	view	76132
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype A2B_tI12_140_h_a v002	view	83191
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A2B_tI24_141_2e_e v002	view	319366
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype A2B_tP3_123_e_a v002	view	72737
Equilibrium crystal structure and energy for AlMgSi in AFLOW crystal prototype A2BC2_hP5_164_d_a_d v001	view	43626
Equilibrium crystal structure and energy for AlCuMg in AFLOW crystal prototype A2BC_oC16_63_f_c_c v001	view	147094
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A30B23_hR53_148_5f_a2c3f v002	view	378899
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype A3B2_hP5_164_ad_d v002	view	55170
Equilibrium crystal structure and energy for AlFeSi in AFLOW crystal prototype A3B2C_cF96_227_f_e_c v001	view	1710131
Equilibrium crystal structure and energy for CuMgSi in AFLOW crystal prototype A3B2C_hP12_194_h_f_a v001	view	84664
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A3B_cF16_225_ac_b v002	view	149744
Equilibrium crystal structure and energy for AlFeSi in AFLOW crystal prototype A3BC2_oP24_60_ad_c_d v001	view	542583
Equilibrium crystal structure and energy for AlSi in AFLOW crystal prototype A4B19_aP46_1_8a_38a v002	view	1818500
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype A4B9_cP52_215_ei_3efgi v002	view	265991
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A5B3_cI64_230_ac_d v002	view	382753
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A5B3_hP16_193_dg_g v002	view	67930
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A5B6_mC22_12_a2i_3i v002	view	164720
Equilibrium crystal structure and energy for AlCuMg in AFLOW crystal prototype A5B6C2_cP39_200_bfi_ek_g v001	view	162411
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A67B41_cP108_221_aeh2il_cfgm v002	view	1263549
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype A6B_oC28_63_efg_c v002	view	620842
Equilibrium crystal structure and energy for AlCuFe in AFLOW crystal prototype A7B2C_tP40_128_egi_h_e v001	view	134280
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype A8B5_cI52_217_cg_ce v002	view	276519
Equilibrium crystal structure and energy for AlFeMgSi in AFLOW crystal prototype A8BC3D6_hP18_189_agh_b_f_i v001	view	165941
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype A9B2_aP22_1_18a_4a v002	view	231739
Equilibrium crystal structure and energy for MgSi in AFLOW crystal prototype A9B5_hP28_176_hi_cef v002	view	99950
Equilibrium crystal structure and energy for AlFeMgSi in AFLOW crystal prototype A9BC3D5_hP18_189_fi_a_g_bh v001	view	136051
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cF136_227_aeg v002	view	1912846
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cF4_225_a v002	view	70602
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cF4_225_a v002	view	167266
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cF4_225_a v002	view	85400
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cF4_225_a v002	view	69752
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cF8_227_a v002	view	146799
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cI12_229_d v002	view	251120
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cI16_206_c v002	view	111903
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cI2_229_a v002	view	75976
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cI2_229_a v002	view	65499
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cI2_229_a v002	view	85326
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cI82_217_acgh v002	view	384975
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cP46_223_cik v002	view	370090
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP1_191_a v002	view	58390
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_hP2_194_c v002	view	81792
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_hP2_194_c v002	view	51403
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP2_194_c v002	view	85621
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP40_191_hjmno v002	view	139140
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP4_194_f v002	view	87019
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP58_164_2d3i3j v002	view	132639
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP68_194_ef2h2kl v002	view	150320
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_mC164_15_e20f v002	view	463903
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_mC16_12_4i v002	view	144885
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_oC92_63_ce2f2g3h v002	view	226392
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_oF16_69_gh v002	view	149597
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_tI4_141_a v002	view	58876
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_tI8_139_h v002	view	96075
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_tP106_137_a5g4h v002	view	192670
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP28_136_f2ij v002	view	295145
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype AB2_cF24_227_a_d v002	view	319145
Equilibrium crystal structure and energy for CuMg in AFLOW crystal prototype AB2_oF48_70_e_ef v002	view	4243854
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype AB2_tP3_123_a_h v002	view	78038
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	135388
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	134137
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype AB3_cP4_221_a_c v002	view	90848
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype AB3_oP12_47_al_ejoz v002	view	142604
Equilibrium crystal structure and energy for AlMgSi in AFLOW crystal prototype AB4C6_mC22_12_a_2i_3i v001	view	270114
Equilibrium crystal structure and energy for AlFe in AFLOW crystal prototype AB_cP2_221_a_b v002	view	95758
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype AB_cP8_198_a_a v002	view	143151
Equilibrium crystal structure and energy for AlCu in AFLOW crystal prototype AB_mC20_12_a2i_c2i v002	view	128082
Equilibrium crystal structure and energy for AlMgSi in AFLOW crystal prototype ABC_oP12_62_c_c_c v001	view	96062

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Relaxed energy as a function of tilt angle for a symmetric tilt grain boundary within a cubic crystal v003

Creators:
Contributor: brunnels
Publication Year: 2022
DOI: https://doi.org/10.25950/2c59c9d6

Computes grain boundary energy for a range of tilt angles given a crystal structure, tilt axis, and material.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in bcc Fe v001	view	22082845
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in bcc Fe v001	view	68054540
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in bcc Fe v001	view	40081664
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Al v003	view	29333539
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Cu v001	view	39303678
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Fe v001	view	42089254
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Al v001	view	48681353
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Cu v001	view	65691580
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Fe v001	view	77898514

LatticeConstantCubicEnergy__TD_475411767977_007

Equilibrium lattice constant and cohesive energy of a cubic lattice at zero temperature and pressure v007

Creators: Daniel S. Karls and Junhao Li
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/2765e3bf

Equilibrium lattice constant and cohesive energy of a cubic lattice at zero temperature and pressure.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium zero-temperature lattice constant for bcc Al v007	view	64565
Equilibrium zero-temperature lattice constant for bcc Cu v007	view	46319
Equilibrium zero-temperature lattice constant for bcc Fe v007	view	44170
Equilibrium zero-temperature lattice constant for bcc Mg v007	view	46716
Equilibrium zero-temperature lattice constant for bcc Si v007	view	43693
Equilibrium zero-temperature lattice constant for diamond Al v007	view	47353
Equilibrium zero-temperature lattice constant for diamond Cu v007	view	56056
Equilibrium zero-temperature lattice constant for diamond Fe v007	view	58455
Equilibrium zero-temperature lattice constant for diamond Mg v007	view	48139
Equilibrium zero-temperature lattice constant for diamond Si v007	view	51013
Equilibrium zero-temperature lattice constant for fcc Al v007	view	53539
Equilibrium zero-temperature lattice constant for fcc Cu v007	view	65256
Equilibrium zero-temperature lattice constant for fcc Fe v007	view	71117
Equilibrium zero-temperature lattice constant for fcc Mg v007	view	71117
Equilibrium zero-temperature lattice constant for fcc Si v007	view	65228
Equilibrium zero-temperature lattice constant for sc Al v007	view	43046
Equilibrium zero-temperature lattice constant for sc Cu v007	view	63019
Equilibrium zero-temperature lattice constant for sc Fe v007	view	48636
Equilibrium zero-temperature lattice constant for sc Mg v007	view	62651
Equilibrium zero-temperature lattice constant for sc Si v007	view	62560

LatticeConstantHexagonalEnergy__TD_942334626465_005

Equilibrium lattice constants for hexagonal bulk structures at zero temperature and pressure v005

Creators: Daniel S. Karls and Junhao Li
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/c339ca32

Calculates lattice constant of hexagonal bulk structures at zero temperature and pressure by using simplex minimization to minimize the potential energy.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium lattice constants for hcp Al v005	view	870342
Equilibrium lattice constants for hcp Cu v005	view	976976
Equilibrium lattice constants for hcp Fe v005	view	876673
Equilibrium lattice constants for hcp Si v005	view	870562

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Linear thermal expansion coefficient of cubic crystal structures v001

Creators: Mingjian Wen
Contributor: mjwen
Publication Year: 2019
DOI: https://doi.org/10.25950/fc69d82d

This Test Driver uses LAMMPS to compute the linear thermal expansion coefficient at a finite temperature under a given pressure for a cubic lattice (fcc, bcc, sc, diamond) of a single given species.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Linear thermal expansion coefficient of fcc Cu at 293.15 K under a pressure of 0 MPa v001		view	160868964

LinearThermalExpansionCoeffCubic__TD_522633393614_002

Linear thermal expansion coefficient of cubic crystal structures v002

Creators:
Contributor: mjwen
Publication Year: 2024
DOI: https://doi.org/10.25950/9d9822ec

This Test Driver uses LAMMPS to compute the linear thermal expansion coefficient at a finite temperature under a given pressure for a cubic lattice (fcc, bcc, sc, diamond) of a single given species.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Linear thermal expansion coefficient of bcc Fe at 293.15 K under a pressure of 0 MPa v002	view	3079864
Linear thermal expansion coefficient of diamond Si at 293.15 K under a pressure of 0 MPa v002	view	1858648
Linear thermal expansion coefficient of fcc Al at 293.15 K under a pressure of 0 MPa v002	view	5637195

PhononDispersionCurve__TD_530195868545_004

Phonon dispersion relations for an fcc lattice v004

Creators: Matt Bierbaum
Contributor: mattbierbaum
Publication Year: 2019
DOI: https://doi.org/10.25950/64f4999b

Calculates the phonon dispersion relations for fcc lattices and records the results as curves.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Phonon dispersion relations for fcc Al v004		view	88284
Phonon dispersion relations for fcc Cu v004		view	104762

StackingFaultFccCrystal__TD_228501831190_002

Stacking and twinning fault energies of an fcc lattice at zero temperature and pressure v002

Creators:
Contributor: SubrahmanyamPattamatta
Publication Year: 2019
DOI: https://doi.org/10.25950/b4cfaf9a

Intrinsic and extrinsic stacking fault energies, unstable stacking fault energy, unstable twinning energy, stacking fault energy as a function of fractional displacement, and gamma surface for a monoatomic FCC lattice at zero temperature and pressure.

Test	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Stacking and twinning fault energies for fcc Al v002		view	54212185
Stacking and twinning fault energies for fcc Cu v002		view	58596680

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

High-symmetry surface energies in cubic lattices and broken bond model v004

Creators: Matt Bierbaum
Contributor: mattbierbaum
Publication Year: 2019
DOI: https://doi.org/10.25950/6c43a4e6

Calculates the surface energy of several high symmetry surfaces and produces a broken-bond model fit. In latex form, the fit equations are given by:

E_{FCC} (\vec{n}) = p_1 (4 \left( |x+y| + |x-y| + |x+z| + |x-z| + |z+y| +|z-y|\right)) + p_2 (8 \left( |x| + |y| + |z|\right)) + p_3 (2 ( |x+ 2y + z| + |x+2y-z| + |x-2y + z| + |x-2y-z| + |2x+y+z| + |2x+y-z| +|2x-y+z| +|2x-y-z| +|x+y+2z| +|x+y-2z| +|x-y+2z| +|x-y-2z| ) + c

E_{BCC} (\vec{n}) = p_1 (6 \left( | x+y+z| + |x+y-z| + |-x+y-z| + |x-y+z| \right)) + p_2 (8 \left( |x| + |y| + |z|\right)) + p_3 (4 \left( |x+y| + |x-y| + |x+z| + |x-z| + |z+y| +|z-y|\right)) +c.

In Python, these two fits take the following form:

def BrokenBondFCC(params, index):

import numpy
x, y, z = index
x = x / numpy.sqrt(x**2.+y**2.+z**2.)
y = y / numpy.sqrt(x**2.+y**2.+z**2.)
z = z / numpy.sqrt(x**2.+y**2.+z**2.)

return params[0]*4* (abs(x+y) + abs(x-y) + abs(x+z) + abs(x-z) + abs(z+y) + abs(z-y)) + params[1]*8*(abs(x) + abs(y) + abs(z)) + params[2]*(abs(x+2*y+z) + abs(x+2*y-z) +abs(x-2*y+z) +abs(x-2*y-z) + abs(2*x+y+z) +abs(2*x+y-z) +abs(2*x-y+z) +abs(2*x-y-z) + abs(x+y+2*z) +abs(x+y-2*z) +abs(x-y+2*z) +abs(x-y-2*z))+params[3]

def BrokenBondBCC(params, x, y, z):

import numpy
x, y, z = index
x = x / numpy.sqrt(x**2.+y**2.+z**2.)
y = y / numpy.sqrt(x**2.+y**2.+z**2.)
z = z / numpy.sqrt(x**2.+y**2.+z**2.)

return params[0]*6*(abs(x+y+z) + abs(x-y-z) + abs(x-y+z) + abs(x+y-z)) + params[1]*8*(abs(x) + abs(y) + abs(z)) + params[2]*4* (abs(x+y) + abs(x-y) + abs(x+z) + abs(x-z) + abs(z+y) + abs(z-y)) + params[3]

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Broken-bond fit of high-symmetry surface energies in bcc Fe v004	view	82214
Broken-bond fit of high-symmetry surface energies in fcc Al v004	view	194216
Broken-bond fit of high-symmetry surface energies in fcc Cu v004	view	161354

TriclinicPBCEnergyAndForces__TD_892847239811_003

Potential energy and atomic forces of periodic, non-orthogonal cell of atoms v003

Creators: Daniel S. Karls
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/c3dca28e

Given an extended xyz file corresponding to a non-orthogonal periodic box of atoms, use LAMMPS to compute the total potential energy and atomic forces.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	2650
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4491
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4704
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6258
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4744
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	7877
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4270
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	8761
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4307
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4933
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	10012
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	7362
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4317
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6111
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	3828
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4933
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4456
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6552
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	10307
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	12221
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	11190
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	2945
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	8319
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	3828
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	5816
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4196
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	3976
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4386
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4635
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4933
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4665
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4744
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4923
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4417
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	5595
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6847
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4123
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4267
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6773
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4327
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4247
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4049
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	2429
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4297
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	9939
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6111
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4287
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4466
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4187
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	12000
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	13031
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4575
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4356
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	7362
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4685
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4535
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4496
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	11632
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	8540
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4491
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6037
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4426
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4595
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4665
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	7141
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4535
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4336
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4417
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4417
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4635
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4695
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	6626
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed bcc structure v003	view	4147
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4605
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4396
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	11485
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	7657
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	8393
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	11779
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3534
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3534
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	2871
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4844
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4785
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	10454
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	11411
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	6405
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4217
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	2724
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4456
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4585
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	2871
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4712
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4515
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	12957
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4426
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	13252
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	10012
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	12957
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4754
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4366
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4794
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3166
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4903
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4564
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4625
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4486
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4386
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4665
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4344
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	2798
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4804
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	6552
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4615
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	7951
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4356
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4466
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4376
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4764
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4665
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3902
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	8466
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3018
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4297
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4824
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4575
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4486
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	5522
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	12074
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3755
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3828
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4625
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	6258
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	2798
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	6111
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4933
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4635
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4575
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	7288
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3976
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	6994
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4883
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4496
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	13252
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	5522
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	12736
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4615
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4615
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4535
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3607
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3313
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4525
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4466
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4675
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4555
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4545
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	12000
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4406
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4784
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4595
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4406
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3239
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	11853
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4638
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	5595
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	10160
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4565
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4545
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	3092
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed diamond structure v003	view	4406
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4426
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4336
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4704
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4744
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	7068
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4645
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	3534
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4491
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4565
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	6111
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	2798
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	8834
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4446
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	8614
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	2945
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4491
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4466
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4545
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	4933
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed random structure v003	view	3018
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4804
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4356
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4346
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4396
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	2429
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4417
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	3902
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4564
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	3902
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	3166
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	3387
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	5112
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	10160
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	6773
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4605
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	5080
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4525
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	3976
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4555
Potential energy and atomic forces of periodic, non-orthogonal cell of Si atoms in a perturbed sh structure v003	view	4426

VacancyFormationEnergyRelaxationVolume__TD_647413317626_001

Monovacancy formation energy and relaxation volume for cubic and hcp monoatomic crystals v001

Creators:
Contributor: efuem
Publication Year: 2023
DOI: https://doi.org/10.25950/fca89cea

Computes the monovacancy formation energy and relaxation volume for cubic and hcp monoatomic crystals.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Monovacancy formation energy and relaxation volume for bcc Fe	view	733628
Monovacancy formation energy and relaxation volume for diamond Si	view	338139
Monovacancy formation energy and relaxation volume for fcc Al	view	729358
Monovacancy formation energy and relaxation volume for fcc Cu	view	690413

VacancyFormationMigration__TD_554849987965_001

Vacancy formation and migration energies for cubic and hcp monoatomic crystals v001

Creators:
Contributor: efuem
Publication Year: 2023
DOI: https://doi.org/10.25950/c27ba3cd

Computes the monovacancy formation and migration energies for cubic and hcp monoatomic crystals.

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Vacancy formation and migration energy for bcc Fe	view	5239497
Vacancy formation and migration energy for diamond Si	view	3599233
Vacancy formation and migration energy for fcc Al	view	1546840
Vacancy formation and migration energy for fcc Cu	view	6252442

Errors

ElasticConstantsCubic__TD_011862047401_006

Test	Error Categories	Link to Error page
Elastic constants for bcc Fe at zero temperature v006	other	view
Elastic constants for bcc Mg at zero temperature v006	other	view

ElasticConstantsHexagonal__TD_612503193866_004

Test	Error Categories	Link to Error page
Elastic constants for hcp Al at zero temperature v004	other	view
Elastic constants for hcp Cu at zero temperature v004	other	view
Elastic constants for hcp Fe at zero temperature v004	other	view
Elastic constants for hcp Si at zero temperature v004	other	view

EquilibriumCrystalStructure__TD_457028483760_000

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_cF136_227_aeg v000	other	view
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_hP2_194_c v000	other	view
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hP68_194_ef2h2kl v000	other	view

EquilibriumCrystalStructure__TD_457028483760_002

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype A5B3_oC32_63_ceg_cg v002	other	view
Equilibrium crystal structure and energy for Si in AFLOW crystal prototype A_hR8_148_cf v002	other	view
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP1_123_a v002	other	view
Equilibrium crystal structure and energy for FeSi in AFLOW crystal prototype AB2_oC48_64_df_2g v002	other	view

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Test	Error Categories	Link to Error page
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in bcc Fe v001	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in bcc Fe v001	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in bcc Fe v001	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Al v001	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Cu v001	other	view
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Fe v001	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Al v001	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Cu v001	other	view
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Fe v001	other	view

LatticeConstantHexagonalEnergy__TD_942334626465_005

Test	Error Categories	Link to Error page
Equilibrium lattice constants for hcp Mg v005	other	view

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Test	Error Categories	Link to Error page
Linear thermal expansion coefficient of bcc Fe at 293.15 K under a pressure of 0 MPa v001	other	view
Linear thermal expansion coefficient of fcc Al at 293.15 K under a pressure of 0 MPa v001	other	view

PhononDispersionCurve__TD_530195868545_004

Test	Error Categories	Link to Error page
Phonon dispersion relations for fcc Al v004	other	view
Phonon dispersion relations for fcc Cu v004	other	view

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

Test	Error Categories	Link to Error page
Broken-bond fit of high-symmetry surface energies in bcc Fe v004	other	view
Broken-bond fit of high-symmetry surface energies in fcc Al v004	other	view
Broken-bond fit of high-symmetry surface energies in fcc Cu v004	other	view

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AlSiMgCuFe.meam
CMakeLists.txt
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elems.meam
kimprovenance.edn
kimspec.edn
library.meam

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MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_002

Verification Check Dashboard

Visualizers (in-page)

BCC Lattice Constant

Cohesive Energy Graph

Diamond Lattice Constant

Dislocation Core Energies

FCC Elastic Constants

FCC Lattice Constant

FCC Stacking Fault Energies

FCC Surface Energies

SC Lattice Constant

Cubic Crystal Basic Properties Table

Tests

Errors

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