EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al-Co system developed by Pun, Yamakov and Mishin (2013) v000

Vesselin Yamakov; Yuri Mishin; Ganga P. Purja Pun

doi:10.25950/48e9959e

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EAM_Dynamo_PunYamakovMishin_2013_NiAlCo__MO_826591359508_000

Interatomic potential for Aluminum (Al), Cobalt (Co), Nickel (Ni).
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Title A single sentence description.	EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al-Co system developed by Pun, Yamakov and Mishin (2013) v000
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.	EAM potential for the Ni-Al-Co system developed by Pun, Yamakov and Mishin (2013) by fitting to experimental and first-principles data. Reasonably good agreement is achieved for physical properties between values predicted by the potential and values known from experiment and/or first-principles calculations. The potential reproduces basic features of the martensitic phase transformation from the B2-ordered high-temperature phase to a tetragonal CuAu-ordered low-temperature phase. The compositional and temperature ranges of this transformation and the martensite microstructure predicted by the potential compare well with existing experimental data. These results indicate that the proposed potential can be used for simulations of the shape memory effect in the Ni–Al–Co system.
Species The supported atomic species.	Al, Co, Ni
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	NIST IPRP (https://www.ctcms.nist.gov/potentials/Al.html#Al-Co-Ni)

Contributor	Ellad B. Tadmor
Maintainer	Ellad B. Tadmor
Developer	Vesselin Yamakov Yuri Mishin Ganga P. Purja Pun
Published on KIM	2018
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Pun GPP, Yamakov V, Mishin Y. Interatomic potential for the ternary Ni–Al–Co system and application to atomistic modeling of the B2–L1 0 martensitic transformation. Modelling and Simulation in Materials Science and Engineering. 2015;23(6):065006. doi:10.1088/0965-0393/23/6/065006 — (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] Yamakov V, Mishin Y, Pun GPP. EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al-Co system developed by Pun, Yamakov and Mishin (2013) v000. OpenKIM; 2018. doi:10.25950/48e9959e [3] Foiles SM, Baskes MI, Daw MS, Plimpton SJ. EAM Model Driver for tabulated potentials with cubic Hermite spline interpolation as used in LAMMPS v005. OpenKIM; 2018. doi:10.25950/68defa36 [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. 77 Citations (64 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 . R. K. Koju and Y. Mishin, “The Role of Grain Boundary Diffusion in the Solute Drag Effect,” Nanomaterials. 2021. link Times cited: 10 Abstract: Molecular dynamics (MD) simulations are applied to study sol… read more Abstract: Molecular dynamics (MD) simulations are applied to study solute drag by curvature-driven grain boundaries (GBs) in Cu–Ag solid solution. Although lattice diffusion is frozen on the MD timescale, the GB significantly accelerates the solute diffusion and alters the state of short-range order in lattice regions swept by its motion. The accelerated diffusion produces a nonuniform redistribution of the solute atoms in the form of GB clusters enhancing the solute drag by the Zener pinning mechanism. This finding points to an important role of lateral GB diffusion in the solute drag effect. A 1.5 at.%Ag alloying reduces the GB free energy by 10–20% while reducing the GB mobility coefficients by more than an order of magnitude. Given the greater impact of alloying on the GB mobility than on the capillary driving force, kinetic stabilization of nanomaterials against grain growth is likely to be more effective than thermodynamic stabilization aiming to reduce the GB free energy. read less A. Bisht, R. K. Koju, Y. Qi, J. Hickman, Y. Mishin, and E. Rabkin, “The impact of alloying on defect-free nanoparticles exhibiting softer but tougher behavior,” Nature Communications. 2021. link Times cited: 12 Y.-S. Lin, G. P. P. Pun, and Y. Mishin, “Development of a physically-informed neural network interatomic potential for tantalum,” Computational Materials Science. 2021. link Times cited: 9 A. Bisht, R. K. Koju, Y. Qi, J. Hickman, Y. Mishin, and E. Rabkin, “Softer but tougher: The impact of alloying on defect-free nanoparticles.” 2021. link Times cited: 0 Abstract: The classic paradigm of physical metallurgy is that the ad… read more Abstract: The classic paradigm of physical metallurgy is that the addition of alloying elements to metals increases their strength. It is less known if the solution-hardening can occur in nano-scale objects, and it is totally unknown how alloying can impact the strength of defect-free faceted nanoparticles. Purely metallic defect-free nanoparticles exhibit an ultra-high strength approaching the theoretical limit. Tested in compression, they deform elastically until the nucleation of the first dislocation, after which they collapse into a pancake shape. Here, we show by experiments and atomistic simulations that the alloying of Ni nanoparticles with Co reduces their ultimate strength. This counter-intuitive solution-softening effect is explained by solute-induced local spatial variations of the resolved shear stress, causing premature dislocation nucleation. At the same time, the subsequent deformation of the particle requires more work, making it tougher. The emerging compromise between strength and toughness can make alloy nanoparticles promising candidates for applications. read less G. P. P. Pun, V. Yamakov, J. Hickman, E. Glaessgen, and Y. Mishin, “Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method,” Physical Review Materials. 2020. link Times cited: 13 Abstract: Interatomic potentials constitute the key component of large… read more Abstract: Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential applicable to both metals and nonmetals. In this paper, we present a modified version of the PINN method that accelerates the potential training process and further improves the transferability of PINN potentials to unknown atomic environments. As an application, a modified PINN potential for Al has been developed by training on a large database of electronic structure calculations. The potential reproduces the reference first-principles energies within 2.6 meV per atom and accurately predicts a wide spectrum of physical properties of Al. Such properties include, but are not limited to, lattice dynamics, thermal expansion, energies of point and extended defects, the melting temperature, the structure and dynamic properties of liquid Al, the surface tensions of the liquid surface and the solid-liquid interface, and the nucleation and growth of a grain boundary crack. Computational efficiency of PINN potentials is also discussed. read less R. K. Koju and Y. Mishin, “Direct Atomistic Modeling of Solute Drag by Moving Grain Boundaries,” Mechanical Engineering eJournal. 2020. link Times cited: 23 Abstract: We show that molecular dynamics (MD) simulations are capable… read more Abstract: We show that molecular dynamics (MD) simulations are capable of reproducing the drag of solute segregation atmospheres by moving grain boundaries (GBs). Although lattice diffusion is frozen out on the MD timescale, the accelerated GB diffusion provides enough atomic mobility to allow the segregated atoms to follow the moving GB. This finding opens the possibility of studying the solute drag effect with atomic precision using the MD approach. We demonstrate that a moving GB activates diffusion and alters the short-range order in the lattice regions swept during its motion. It is also shown that a moving GB drags an atmosphere of non-equilibrium vacancies, which accelerate diffusion in surrounding lattice regions. read less R. K. Koju and Y. Mishin, “Relationship between grain boundary segregation and grain boundary diffusion in Cu-Ag alloys,” arXiv: Materials Science. 2020. link Times cited: 12 Abstract: While it is known that alloy components can segregate to gra… read more Abstract: While it is known that alloy components can segregate to grain boundaries (GBs), and that the atomic mobility in GBs greatly exceeds the atomic mobility in the lattice, little is known about the effect of GB segregation on GB diffusion. Atomistic computer simulations offer a means of gaining insights into the segregation-diffusion relationship by computing the GB diffusion coefficients of the alloy components as a function of their segregated amounts. In such simulations, thermodynamically equilibrium GB segregation is prepared by a semi-grand canonical Monte Carlo method, followed by calculation of the diffusion coefficients of all alloy components by molecular dynamics. As a demonstration, the proposed methodology is applied to a GB is the Cu-Ag system. The GB diffusivities obtained exhibit non-trivial composition dependencies that can be explained by site blocking, site competition, and the onset of GB disordering due to the premelting effect. read less M. Diehl et al., “Solving Material Mechanics and Multiphysics Problems of Metals with Complex Microstructures Using DAMASK—The Düsseldorf Advanced Material Simulation Kit,” Advanced Engineering Materials. 2020. link Times cited: 13 Abstract: Predicting process–structure and structure–property relation… read more Abstract: Predicting process–structure and structure–property relationships are the key tasks in materials science and engineering. Central to both research directions is the internal material structure. In the case of metallic materials for structural applications, this internal structure, the microstructure, is the collective ensemble of all equilibrium and nonequilibrium lattice imperfections. Continuum models to derive process–structure and structure–property relationships are based on two ingredients: 1) quantitative state variables that capture the essential features of the material's state and 2) kinetic equations for the state that describe its evolution under load. Successful models, that is, models that are of practical use, depend on state variables and corresponding evolution laws that are sufficiently representative for the microstructure and that are able to describe the phenomena of interest. The development of software tools capable to integrate these different aspects to get a holistic view of process–structure–property relationships requires joint efforts from specialists in different disciplines and a long‐term perspective. The Düsseldorf Advanced Material Simulation Kit (DAMASK) is such a tool. In this overview article published on the occasion of Advanced Engineering Materials' 20th anniversary, some representative application examples which demonstrate how DAMASK can be used to study metallic microstructures at different length scales are presented. read less X. Lu, P. Yang, J. Luo, J. Ren, H. Xue, and Y. Ding, “Tensile mechanical performance of Ni–Co alloy nanowires by molecular dynamics simulation,” RSC Advances. 2019. link Times cited: 17 Abstract: In this present contribution, tensile mechanical properties … read more Abstract: In this present contribution, tensile mechanical properties of Ni–Co alloy nanowires with Co content from 0 to 20% were studied by molecular dynamics. The simulation results show the alloy nanowire with the Co content of 5% has the highest yield value of 9.72 GPa. In addition, more Frank dislocations were generated during the loading process to improve the performance of the alloy nanowire. The Young's modulus increases little by little from 105.68 to 179.78 GPa with the increase of Co content. Secondly, with the increase of temperature, the yield strength gradually decreases to 2.13 GPa. Young's modulus tends to decrease linearly from 170.7 GPa to 48.21 GPa. At the temperatures of 500 K and 700 K, it is easier to form Frank dislocation and Hirth dislocation, respectively, in the loading process. The peak value of the radial distribution function decreases and the number of peaks decreases, indicating the disappearance of the ordered structure. Finally, after the introduction of the surface and inner void, the yield strength of the nanowire drops about to 8.97 and 6.6 GPa, respectively, and the yield strains drop to 0.056 and 0.043. In the case of the existence of internal void, perfect dislocation and Hirth dislocation can be observed in the structure. read less S. Madireddy et al., “Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection,” Scientific Reports. 2019. link Times cited: 16 E. Levo, F. Granberg, D. Utt, K. Albe, K. Nordlund, and F. Djurabekova, “Radiation stability of nanocrystalline single-phase multicomponent alloys,” Journal of Materials Research. 2019. link Times cited: 9 Abstract: In search of materials with better properties, polycrystalli… read more Abstract: In search of materials with better properties, polycrystalline materials are often found to be superior to their respective single crystalline counterparts. Reduction of grain size in polycrystalline materials can drastically alter the properties of materials. When the grain sizes reach the nanometer scale, the improved mechanical response of the materials make them attractive in many applications. Multicomponent solid-solution alloys have shown to have a higher radiation tolerance compared with pure materials. Combining these advantages, we investigate the radiation tolerance of nanocrystalline multicomponent alloys. We find that these alloys withstand a much higher irradiation dose, compared with nanocrystalline Ni, before the nanocrystallinity is lost. Some of the investigated alloys managed to keep their nanocrystallinity for twice the irradiation dose as pure Ni. read less L. Huber, R. Hadian, B. Grabowski, and J. Neugebauer, “A machine learning approach to model solute grain boundary segregation,” npj Computational Materials. 2018. link Times cited: 83 S. Yuan, Z. Jiang, J. Liu, Y. Tang, and Y. Zhang, “Nano-twinning in a γ′ precipitate strengthened Ni-based superalloy,” Materials Research Letters. 2018. link Times cited: 19 Abstract: ABSTRACT Twinning has been found to be a dominate mechanism … read more Abstract: ABSTRACT Twinning has been found to be a dominate mechanism in the γ′ precipitate strengthened Ni-based superalloys during service at intermediate temperatures. Here, high-resolution transmission electron microscopy and atomistic simulations have been combined to show that the twin nucleation process can be facilitated by Co replacing a fraction of Al in the γ′ precipitates, due to the negative binding energy of Co–Co atoms. The study further reveals that the presence of Co promotes a new twinning pathway featured with nucleation of one complex stacking fault (CSF) on the middle plane in between two separated CSFs. GRAPHICAL ABSTRACT IMPACT STATEMENT We demonstrate that Co in the γ′ precipitates promotes a new twinning pathway featured with nucleation of one CSF on the middle plane between two separated CSFs. read less K. V. Reddy and S. Pal, “Analysis of deformation behaviour of Al–Ni–Co thin film coated aluminium during nano-indentation: a molecular dynamics study,” Molecular Simulation. 2018. link Times cited: 16 Abstract: ABSTRACT In the present work, molecular dynamics simulations… read more Abstract: ABSTRACT In the present work, molecular dynamics simulations of AlNiCo metallic film deposition on FCC Al substrate and subsequently nano-indentation on the same specimen considering different indenter velocities have been performed using Embedded Atom Method (EAM) potential. The mechanical properties and deformation behaviour of AlNiCo thin film deposited Al substrate is investigated subjected to simulated nano-indentation test. It has been found that indenter velocity significantly influences the calculated hardness of the thin film coated substrate specimen and faster indentation process increases the hardness of the specimen. This finding has been rationalised by correlating with the generation of various full and partial dislocations and their interactions during the nano-indentation process. Sessile dislocations such as stair-rod and Frank partials are found to aid/help the strain hardening phenomena. Furthermore, the effect of indenter velocity on the pile-up formation during the nano-indentation process is also investigated here and it is observed that the amount of pile-up reduces as indenter velocity increases. read less R. K. Koju, K. Darling, K. Solanki, and Y. Mishin, “Atomistic modeling of capillary-driven grain boundary motion in Cu-Ta alloys,” Acta Materialia. 2018. link Times cited: 41 A. Zinovev, M. G. Bapanina, R. Babicheva, N. Enikeev, S. Dmitriev, and K. Zhou, “Deformation of nanocrystalline binary aluminum alloys with segregation of Mg, Co and Ti at grain boundaries,” Physics of Metals and Metallography. 2017. link Times cited: 6 S. Zhao, G. Velişa, H. Xue, H. Bei, W. J. Weber, and Y. Zhang, “Suppression of vacancy cluster growth in concentrated solid solution alloys,” Acta Materialia. 2017. link Times cited: 41 Y. Li, M. Lv, and H. Liang, “Local Structural Arrangement of Amorphous Al-Ni-Co Alloy during Uniaxial Tension: A Molecular Dynamics Study,” Materials Transactions. 2016. link Times cited: 2 Abstract: The local structural arrangement of the amorphous alloy syst… read more Abstract: The local structural arrangement of the amorphous alloy system Al45Ni50Co5, subject to uniaxial tensile strain, was investigated using molecular dynamics simulation. The amorphous phase of the alloy system did not change in the process. The degree of icosahedral order quanti ed by the Honeycutt-Andersen structural type, 1551, plays an important role in the formation and expansion of the shear transition zones in this amorphous system. The structural types 1551, 1441, and 1661 are found to evolve during the deformation process. [doi:10.2320/matertrans.MG201621] read less J. Hickman and Y. Mishin, “Disjoining potential and grain boundary premelting in binary alloys,” Physical Review B. 2016. link Times cited: 24 Abstract: Many grain boundaries (GBs) in crystalline materials develop… read more Abstract: Many grain boundaries (GBs) in crystalline materials develop highly disordered, liquidlike structures at high temperatures. In alloys, this premelting effect can be fueled by solute segregation and can occur at lower temperatures than in single-component systems. A premelted GB can be modeled by a thin liquid layer located between two solid-liquid interfaces interacting by a disjoining potential. We propose a single analytical form of the disjoining potential describing repulsive, attractive, and intermediate interactions. The potential predicts a variety of premelting scenarios, including thin-to-thick phase transitions. The potential is verified by atomistic computer simulations of premelting in three different GBs in Cu-Ag alloys employing a Monte Carlo technique with an embedded atom potential. The disjoining potential has been extracted from the simulations by analyzing GB width fluctuations. The simulations confirm all shapes of the disjoining potential predicted by the analytical model. One of the GBs was found to switch back and forth between two (thin and thick) states, confirming the existence of thin-to-thick phase transformations in this system. The proposed disjoining potential also predicts the possibility of a cascade of thin-to-thick transitions caused by compositional oscillations (patterning) near solid-liquid interfaces. read less W. Nöhring and W. Curtin, “Thermodynamic properties of average-atom interatomic potentials for alloys,” Modelling and Simulation in Materials Science and Engineering. 2016. link Times cited: 17 Abstract: The atomistic mechanisms of deformation in multicomponent ra… read more Abstract: The atomistic mechanisms of deformation in multicomponent random alloys are challenging to model because of their extensive structural and compositional disorder. For embedded-atom-method interatomic potentials, a formal averaging procedure can generate an average-atom EAM potential and this average-atom potential has recently been shown to accurately predict many zero-temperature properties of the true random alloy. Here, the finite-temperature thermodynamic properties of the average-atom potential are investigated to determine if the average-atom potential can represent the true random alloy Helmholtz free energy as well as important finite-temperature properties. Using a thermodynamic integration approach, the average-atom system is found to have an entropy difference of at most 0.05 kB/atom relative to the true random alloy over a wide temperature range, as demonstrated on FeNiCr and Ni85Al15 model alloys. Lattice constants, and thus thermal expansion, and elastic constants are also well-predicted (within a few percent) by the average-atom potential over a wide temperature range. The largest differences between the average atom and true random alloy are found in the zero temperature properties, which reflect the role of local structural disorder in the true random alloy. Thus, the average-atom potential is a valuable strategy for modeling alloys at finite temperatures. read less T. Wallace et al., “Computational Modeling and Experimental Characterization of Martensitic Transformations in Nicoal for Self‐Sensing Materials.” 2015. link Times cited: 0 R. Liu, S. Li, L. Chen, J. Li, and L. Kong, “Investigation of Edge Dislocation Mobility in Ni-Co Solid Solutions by Molecular Dynamics Simulation,” Materials Today Communications. 2023. link Times cited: 0 A. Khoei, M. R. Seddighian, and A. R. Sameti, “Machine learning-based multiscale framework for mechanical behavior of nano-crystalline structures,” International Journal of Mechanical Sciences. 2023. link Times cited: 0 Y. Li and W. Qiang, “Compositional effects on the antiphase boundary energies in B2-type NiTi and NiTi-based high-entropy intermetallics,” Materials Chemistry and Physics. 2023. link Times cited: 0 M. Tahani, E. Postek, and T. Sadowski, “Investigating the Influence of Diffusion on the Cohesive Zone Model of the SiC/Al Composite Interface,” Molecules. 2023. link Times cited: 1 Abstract: Modeling metal matrix composites in finite element software … read more Abstract: Modeling metal matrix composites in finite element software requires incorporating a cohesive zone model (CZM) to represent the interface between the constituent materials. The CZM determines the behavior of traction–separation (T–S) in this region. Specifically, when a diffusion zone is formed due to heat treatment, it becomes challenging to determine experimentally the equivalent mechanical properties of the interface. Additionally, understanding the influence of heat treatment and the creation of a diffusion zone on the T–S law is crucial. In this study, the molecular dynamics approach was employed to investigate the effect of the diffusion region formation, resulting from heat treatment, on the T–S law at the interface of a SiC/Al composite in tensile, shear, and mixed-mode loadings. It was found that the formation of a diffusion layer led to an increase in tensile and shear strengths and work of separation compared with the interfaces without heat treatment. However, the elastic and shear moduli were not significantly affected by the creation of the diffusion layer. Moreover, the numerical findings indicated that the shear strength in the diffusion region was higher when compared with the shear strength of the {111} slip plane within the fcc aluminum component of the composite material. Therefore, in the diffusion region, crack propagation did not occur in the pure shear loading case; however, shear sliding was observed at the aluminum atomic layers. read less L. Jiao et al., “Atomistic insights into the synergistic effect of nanotwins and nano-precipitates on the mechanical behavior of superalloys,” Mechanics of Materials. 2023. link Times cited: 0 M. Wakeda, T. Osada, and T. Ohmura, “Atomistic analysis of temperature-dependent dislocation dynamics in Ni3Al-based intermetallic alloys,” Materials Today Communications. 2023. link Times cited: 2 Z. Ma and Z. Pan, “Efficient machine learning of solute segregation energy based on physics-informed features,” Scientific Reports. 2023. link Times cited: 0 Z. Zhang and C. Deng, “Solid solution softening in single crystalline metal nanowires studied by atomistic simulations,” Physical Review Materials. 2023. link Times cited: 0 Y.-F. Wu, W. Yu, and S. Shen, “Developing a variable charge potential for Hf/Nb/Ta/Ti/Zr/O system via machine learning global optimization,” Materials & Design. 2023. link Times cited: 1 X. Lu et al., “Strengthening mechanism of NiCoAl alloy induced by nanotwin under Hall-Petch effect,” International Journal of Mechanical Sciences. 2023. link Times cited: 1 G. Li, T. Yu, P. Wu, and M. Chen, “Molecular dynamics simulation of phase transformation and wear behavior of Ni35Al30Co35 high temperature shape memory alloy,” Wear. 2023. link Times cited: 0 N. Tuchinda and C. Schuh, “Triple junction solute segregation in Al-based polycrystals,” Physical Review Materials. 2023. link Times cited: 3 M. Tahani, E. Postek, L. Motevalizadeh, and T. Sadowski, “Effect of Vacancy Defect Content on the Interdiffusion of Cubic and Hexagonal SiC/Al Interfaces: A Molecular Dynamics Study,” Molecules. 2023. link Times cited: 5 Abstract: The mechanical properties of ceramic–metal nanocomposites ar… read more Abstract: The mechanical properties of ceramic–metal nanocomposites are greatly affected by the equivalent properties of the interface of materials. In this study, the effect of vacancy in SiC on the interdiffusion of SiC/Al interfaces is investigated using the molecular dynamics method. The SiC reinforcements exist in the whisker and particulate forms. To this end, cubic and hexagonal SiC lattice polytypes with the Si- and C-terminated interfaces with Al are considered as two samples of metal matrix nanocomposites. The average main and cross-interdiffusion coefficients are determined using a single diffusion couple for each system. The interdiffusion coefficients of the defective SiC/Al are compared with the defect-free SiC/Al system. The effects of temperature, annealing time, and vacancy on the self- and interdiffusion coefficients are investigated. It is found that the interdiffusion of Al in SiC increases with the increase in temperature, annealing time, and vacancy. read less M. Tahani, E. Postek, and T. Sadowski, “Molecular Dynamics Study of Interdiffusion for Cubic and Hexagonal SiC/Al Interfaces,” Crystals. 2022. link Times cited: 5 Abstract: The mechanical properties of the SiC/Al interface are crucia… read more Abstract: The mechanical properties of the SiC/Al interface are crucial in estimating the overall strength of this ceramic-metal composite. The present work investigates the interdiffusion at the SiC/Al interface using molecular dynamics simulations. One cubic and one hexagonal SiC with a higher probability of orientations in contact with Al are examined as two samples of metal-matrix nanocomposites with whisker and particulate reinforcements. These reinforcements with the Si- and C-terminated surfaces of the SiC/Al interfaces are also studied. The average main and cross-interdiffusion coefficients are evaluated using a single diffusion couple for each system. The effect of temperature and annealing time are analysed on the self- and interdiffusion coefficients. It is found that the diffusion of Al in SiC is similar in cubic and hexagonal SiC and as expected, the interdiffusion coefficient increases as the temperature and annealing time increase. The model after diffusion can be used to evaluate the overall mechanical properties of the interface region in future studies. read less 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 J. Zhou, Y. Yang, and Y. Yu, “Revealing mechanical property–strengthening micro-mechanism of Ni/Ni3Al-based alloys by molecular dynamics simulation,” Journal of Molecular Modeling. 2022. link Times cited: 1 B. Xu et al., “Exploring the influence of percolation on vacancy-mediated diffusion in CoCrNi multi-principal element alloys,” Materials & Design. 2022. link Times cited: 4 F. Schwarz and R. Spolenak, “An MD-study on changing the elemental distribution and composition by alloying to control front propagation in Al–Ni multilayers,” Journal of Applied Physics. 2022. link Times cited: 3 Abstract: To cover the wide range of applications of reactive multilay… read more Abstract: To cover the wide range of applications of reactive multilayers, it is necessary to have the ability to vary and control their front propagation velocities as well as their maximum reaction temperatures. In this paper, Molecular Dynamics simulations are used to study the influence of Al alloying, Ni alloying, and Co alloying on Al–Ni multilayers. In the case of alloying with Al and Ni, the iso-stoichiometric case where both the Al and the Ni layers are alloyed is first studied. In the second step, the stoichiometry is varied by alloying only one of the two layers with the other element. This allows for achieving very small front propagation velocities. Furthermore, the Ni layer is alloyed with Co and the whole range from a binary Al–Ni to the binary Al–Co system is studied. The front propagation velocity does not change linearly with the alloying fraction and reaches a minimum where the Ni/Co alloy changes from a face centered cubic to a hexagonal close packed lattice. read less A.-V. Pham, T. Fang, V.-T. Nguyen, and T.-H. Chen, “Mechanical characteristics of Ni50Co50/Ni substrate during indentation by molecular dynamics,” Modelling and Simulation in Materials Science and Engineering. 2022. link Times cited: 2 Abstract: Coating an alloys film onto a metallic surface could dramati… read more Abstract: Coating an alloys film onto a metallic surface could dramatically improve the surface quality. This report studies the microstructure and intermixing phenomena of Ni50Co50 film deposited on Ni(001) substrate with flat, asperity and trench Ni surfaces by molecular dynamics (MD) simulation. The effects of the film thickness and loading velocity on the mechanical properties and deformation behaviours of the sample are also surveyed by indentation. The results represent that the intermixing and lattice structure of the film is enhanced after annealing. Moreover, the sample hardness is improved as the deposited Ni50Co50 film when the film thickness rising from 18 to 38 Å. In contrast, the structure transformation rate and dislocations density of the sample decrease when the Ni50Co50 film becomes thicker. Interestingly, the plastic deformation rate and dislocation density of the sample at the trench surface are higher than the flat one. Besides, the increase of the loading velocity gives rise to the plastic deformation and the local stress rates. The dislocation density of the Ni50Co50/Ni sample is reduced if the loading speed is high enough. read less T.-N. Vu, V.-T. Pham, V.-T. Nguyen, and T. Fang, “Interfacial strength and deformation mechanism of Ni/Co multilayers under uniaxial tension using molecular dynamics simulation,” Materials Today Communications. 2021. link Times cited: 9 R. Campos, S. Shinzato, A. Ishii, S. Nakamura, and S. Ogata, “Database-driven semigrand canonical Monte Carlo method: Application to segregation isotherm on defects in alloys.,” Physical review. E. 2021. link Times cited: 0 Abstract: The application of existing semigrand canonical ensemble Mon… read more Abstract: The application of existing semigrand canonical ensemble Monte Carlo algorithms to alloys requires the chemical potential difference values between pairs of atomic species in the alloys as inputs. However, finding the appropriate values for a target system at a desired temperature and bulk composition is a time-consuming task consisting of multiple test runs to determine the chemical potential differences. This problem becomes more serious when dealing with systems containing three or more atomic species, such as medium- and high-entropy alloys, due to the increase of the number of chemical potential differences that need to be calculated. Here we propose a method for sampling from the semigrand canonical ensemble that relies on energy databases acting as an external atomic reservoir at the desired temperature and composition. Given these energy databases, the desired bulk composition and corresponding chemical potential differences can be satisfied in a "single" Monte Carlo simulation. Moreover, the energy databases shed light on the underlying energetics of alloys, reflecting their local chemical ordering. We demonstrate the validity of this method using analyses of segregation isotherms at grain boundaries and dislocations in two alloy systems: Fe-1-at.-%-Si and NiCoCr medium-entropy alloy. We also discuss the possibly relevant information contained in such energy databases. read less Q. Gao et al., “Shear deformation mechanical performance of Ni–Co alloy nanoplate by molecular dynamics simulation,” Modern Physics Letters B. 2021. link Times cited: 3 Abstract: Ni–Co alloy has great advantages in the fields of micro-elec… read more Abstract: Ni–Co alloy has great advantages in the fields of micro-electromechanical systems and aerospace, however, the lack of micro-deformation mechanism restricts its industrial application. Herein, the deformation mechanism and microstructure evolution of Ni–Co alloy nanoplate under shear loading are investigated by MD. The yield strength increases gradually with the increase of the velocity, and the highest shear modulus is 111.43 GPa. The stress concentration leads to the nucleation and expansion of the dislocation, and the stacking fault expands with the dislocation motion, swallowing most of the disordered atoms. By Dislocation Extraction Algorithm (DXA), it is found that Shockley and Perfect dislocations make a major role, and the interactions between dislocations are responsible for the high mechanical properties. As the temperature increases, the yield strength decreases significantly, the stress fluctuations in the plastic phase at 100 K and 200 K are greater compared to other temperatures. Meanwhile, the coherence of the dislocations motion decreases, and the atoms in the stacking faults are scattered, leading to the decreasing of area. The above results are helpful for the design and control of nanoelectronic facilities and provide a significant guide for the industrial applications of Ni–Co alloy nanoplate. read less Y.-C. Yang, C. Liu, C.-Y. Lin, and Z. Xia, “The effect of local atomic configuration in high-entropy alloys on the dislocation behaviors and mechanical properties,” Materials Science and Engineering A-structural Materials Properties Microstructure and Processing. 2021. link Times cited: 18 L.-F. Zhu, J. Janssen, S. Ishibashi, F. Körmann, B. Grabowski, and J. Neugebauer, “A fully automated approach to calculate the melting temperature of elemental crystals,” Computational Materials Science. 2021. link Times cited: 17 A. Atila, M. Kbirou, S. Ouaskit, and A. Hasnaoui, “On the presence of nanoscale heterogeneity in Al70Ni15Co15 metallic glass under pressure,” Journal of Non-crystalline Solids. 2020. link Times cited: 10 T.-X. Bui, T. Fang, and C.-I. Lee, “Effects of flaw shape and size on fracture toughness and destructive mechanism inside Ni15Al70Co15 metallic glass,” Computational Materials Science. 2020. link Times cited: 12 M. Ghaemi and R. Tavakoli, “Universal correlation between the thermodynamic potentials and some physical quantities of metallic glasses as a function of cooling rate during molecular dynamics simulation,” Journal of Non-crystalline Solids. 2020. link Times cited: 2 D. Choudhuri and A. CampBell, “Interface dominated deformation mechanisms in two-phase fcc/B2 nanostructures: Nishiyama-Wasserman vs. Kurdjumov-Sachs interfaces,” Computational Materials Science. 2020. link Times cited: 13 D. Choudhuri, S. G. Srinivasan, and R. Mishra, “Deformation of lamellar FCC-B2 nanostructures containing Kurdjumov-Sachs interfaces: Relation between interfacial structure and plasticity,” International Journal of Plasticity. 2020. link Times cited: 29 J. Mianroodi et al., “Atomistic phase field chemomechanical modeling of dislocation-solute-precipitate interaction in Ni–Al–Co,” Acta Materialia. 2019. link Times cited: 51 Y. Zhou, X. J. Han, and J. Li, “Transport properties and abnormal breakdown of the Stokes-Einstein relation in computer simulated Al72Ni16Co12 metallic melt,” Journal of Non-Crystalline Solids. 2019. link Times cited: 7 J. Fürnkranz, “Publication list,” Journal of Physics: Conference Series. 2019. link Times cited: 10 Abstract: PUBLICATION LIST of VICTOR MANUEL VILLANUEVA SANDOVAL availa… read more Abstract: PUBLICATION LIST of VICTOR MANUEL VILLANUEVA SANDOVAL available in this PDF. read less D. Choudhuri et al., “Enhancing strength and strain hardenability via deformation twinning in fcc-based high entropy alloys reinforced with intermetallic compounds,” Acta Materialia. 2019. link Times cited: 139 H. N. Pishkenari, F. S. Yousefi, and A. Taghibakhshi, “Determination of surface properties and elastic constants of FCC metals: a comparison among different EAM potentials in thin film and bulk scale,” Materials Research Express. 2018. link Times cited: 22 Abstract: Three independent elastic constants C11, C12, and C44 were c… read more Abstract: Three independent elastic constants C11, C12, and C44 were calculated and compared using available potentials of eight different metals with FCC crystal structure; Gold, Silver, Copper, Nickel, Platinum, Palladium, Aluminum and Lead. In order to calculate the elastic constants, the second derivative of the energy density of each system was calculated with respect to different directions of strains. Each set of the elastic constants of the metals in bulk scale was compared with experimental results, and the average relative error was for each was calculated and compared with other available potentials. Then, using the Voigt-Reuss-Hill method, approximated values for Young and shear moduli and Poisson’s ratio of the FCC metals in the bulk scale were found for each potential. Furthermore, to observe the surface effects on the metals in nanoscale, surface elastic constants of the thin films of the metals have been calculated. In the study of the thin films of materials in nanoscale, the number of surface atoms is considerable compared to all atoms of the object. This leads to an increase in the surface effects, which influence the elastic properties. By considering this fact and employing related definitions and equations, the properties of the thin films of the metals were calculated, and the surface effects for different crystallographic directions were compared. Subsequently, in some cases, comparisons among characteristics of the metals in the thin film and bulk material were made. read less M. Kbirou, S. Trady, A. Hasnaoui, and M. Mazroui, “Short and medium-range orders in Co3Al metallic glass,” Chemical Physics. 2018. link Times cited: 11 K. Choudhary, A. Biacchi, S. Ghosh, L. Hale, A. Walker, and F. Tavazza, “High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields,” Journal of Physics: Condensed Matter. 2018. link Times cited: 16 Abstract: In this work, we present an open access database for surface… read more Abstract: In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17 506 surface energies and 1000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites: www.ctcms.nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis. Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties. read less S. Zhao, Y. Zhang, and W. J. Weber, “Stability of vacancy-type defect clusters in Ni based on first-principles and molecular dynamics simulations☆,” Scripta Materialia. 2018. link Times cited: 10 M. Kbirou, M. Mazroui, and A. Hasnaoui, “Atomic packing and fractal behavior of Al-Co metallic glasses,” Journal of Alloys and Compounds. 2018. link Times cited: 17 R. Babicheva et al., “Effect of grain boundary segregation of Co or Ti on cyclic deformation of aluminium bi-crystals,” International Journal of Fatigue. 2017. link Times cited: 23 R. Babicheva et al., “Effect of grain boundary segregation on the deformation mechanisms and mechanical properties of nanocrystalline binary aluminum alloys,” Computational Materials Science. 2016. link Times cited: 49 R. K. Koju, K. Darling, L. Kecskes, and Y. Mishin, “Zener Pinning of Grain Boundaries and Structural Stability of Immiscible Alloys,” JOM. 2016. link Times cited: 51 S. Kalidindi, J. A. Gomberg, Z. Trautt, and C. Becker, “Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets,” Nanotechnology. 2015. link Times cited: 39 Abstract: Structure quantification is key to successful mining and ext… read more Abstract: Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations. read less L. Lilensten et al., “Segregation of Solutes at Dislocations: A New Alloy Design Parameter for Advanced Superalloys.” 2020. link Times cited: 7 K. Choudhary, B. L. DeCost, L. Major, K. Butler, J. Thiyagalingam, and F. Tavazza, “Unified Graph Neural Network Force-field for the Periodic Table for Solids,” Digital Discovery. 2022. link Times cited: 6 Abstract: Classical force fields (FF) based on machine learning (ML) m… read more Abstract: Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted for specific systems... read less O. Bindech, C. Goyhenex, and É. Gaudry, “A tight-binding atomistic approach for point defects and surfaces applied to the o-Al13Co4 quasicrystalline approximant,” Computational Materials Science. 2021. link Times cited: 0 L. Xu, L. Casillas-Trujillo, Y. Gao, and H. Xu, “Compositional effects on stacking fault energies in Ni-based alloys using first-principles and atomistic simulations,” Computational Materials Science. 2021. link Times cited: 5 D. Farkas and A. Caro, “Model interatomic potentials for Fe–Ni–Cr–Co–Al high-entropy alloys,” Journal of Materials Research. 2020. link Times cited: 76 Abstract: A set of embedded atom model (EAM) interatomic potentials wa… read more Abstract: A set of embedded atom model (EAM) interatomic potentials was developed to represent highly idealized face-centered cubic (FCC) mixtures of Fe–Ni–Cr–Co–Al at near-equiatomic compositions. Potential functions for the transition metals and their crossed interactions are taken from our previous work for Fe–Ni–Cr–Co–Cu [D. Farkas and A. Caro: J. Mater. Res. 33 (19), 3218–3225, 2018], while cross-pair interactions involving Al were developed using a mix of the component pair functions fitted to known intermetallic properties. The resulting heats of mixing of all binary equiatomic random FCC mixtures not containing Al is low, but significant short-range ordering appears in those containing Al, driven by a large atomic size difference. The potentials are utilized to predict the relative stability of FCC quinary mixtures, as well as ordered L1_2 and B2 phases as a function of Al content. These predictions are in qualitative agreement with experiments. This interatomic potential set is developed to resemble but not model precisely the properties of this complex system, aiming at providing a tool to explore the consequences of the addition of a large size-misfit component into a high entropy mixture that develops multiphase microstructures. read less L. Karkina, I. Karkin, A. Kuznetsov, and Y. Gornostyrev, “Alloying Element Segregation and Grain Boundary Reconstruction, Atomistic Modeling.” 2019. link Times cited: 8 Abstract: Grain boundary (GB) segregation is an important phenomenon t… read more Abstract: Grain boundary (GB) segregation is an important phenomenon that affects many physical properties, as well as microstructure of polycrystals. The segregation of solute atoms on GBs and its effect on GB structure in Al were investigated using two approaches: First principles total energy calculations and the finite temperature large-scale atomistic modeling within hybrid MD/MC approach comprising molecular dynamics and Monte Carlo simulations. We show that the character of chemical bonding is essential in the solute–GB interaction, and that formation of directed quasi-covalent bonds between Si and Zn solutes and neighboring Al atoms causes a significant reconstruction of the GB structure involving a GB shear-migration coupling. For the solutes that are acceptors of electrons in the Al matrix and have a bigger atomic size (such as Mg), the preferred position is determined by the presence of extra volume at the GB and/or reduced number of the nearest neighbors; in this case, the symmetric GB keeps its structure. By using MD/MC approach, we found that GBs undergo significant structural reconstruction during segregation, which can involve the formation of singleor double-layer segregations, GB splitting, and coupled shear-migration, depending on the details of interatomic interactions. read less S. Mahmoud and N. Mousseau, “Long-time point defect diffusion in ordered nickel-based binary alloys: How small kinetic differences can lead to completely long-time structural evolution,” Materialia. 2018. link Times cited: 15 Y. Hu and T. Rupert, “Atomistic modeling of interfacial segregation and structural transitions in ternary alloys,” Journal of Materials Science. 2018. link Times cited: 19 L. Hale, “Comparing Modeling Predictions of Aluminum Edge Dislocations: Semidiscrete Variational Peierls–Nabarro Versus Atomistics,” JOM. 2018. link Times cited: 7 P. Chowdhury, “Frontiers of Theoretical Research on Shape Memory Alloys: A General Overview,” Shape Memory and Superelasticity. 2018. link Times cited: 16 P. Chowdhury and H. Sehitoglu, “Deformation physics of shape memory alloys – Fundamentals at atomistic frontier,” Progress in Materials Science. 2017. link Times cited: 110 Y. Zhang et al., “Influence of chemical disorder on energy dissipation and defect evolution in advanced alloys,” Journal of Materials Research. 2016. link Times cited: 103 Abstract: Historically, alloy development with better radiation perfor… read more Abstract: Historically, alloy development with better radiation performance has been focused on traditional alloys with one or two principal element(s) and minor alloying elements, where enhanced radiation resistance depends on microstructural or nanoscale features to mitigate displacement damage. In sharp contrast to traditional alloys, recent advances of single-phase concentrated solid solution alloys (SP-CSAs) have opened up new frontiers in materials research. In these alloys, a random arrangement of multiple elemental species on a crystalline lattice results in disordered local chemical environments and unique site-to-site lattice distortions. Based on closely integrated computational and experimental studies using a novel set of SP-CSAs in a face-centered cubic structure, we have explicitly demonstrated that increasing chemical disorder can lead to a substantial reduction in electron mean free paths, as well as electrical and thermal conductivity, which results in slower heat dissipation in SP-CSAs. The chemical disorder also has a significant impact on defect evolution under ion irradiation. Considerable improvement in radiation resistance is observed with increasing chemical disorder at electronic and atomic levels. The insights into defect dynamics may provide a basis for understanding elemental effects on evolution of radiation damage in irradiated materials and may inspire new design principles of radiation-tolerant structural alloys for advanced energy systems. read less S. M. Rassoulinejad-Mousavi, Y. Mao, and Y. Zhang, “Evaluation of Copper, Aluminum and Nickel Interatomic Potentials on Predicting the Elastic Properties,” arXiv: Computational Physics. 2016. link Times cited: 63 Abstract: Choice of appropriate force field is one of the main concern… read more Abstract: Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since, investigations on mechanical behavior of materials at micro/nanoscale has been becoming much more widespread, it is necessary to determine an adequate potential which accurately models the interaction of the atoms for desired applications. In this framework, reliability of multiple embedded atom method based interatomic potentials for predicting the elastic properties was investigated. Assessments were carried out for different copper, aluminum and nickel interatomic potentials at room temperature which is considered as the most applicable case. Examined force fields for the three species were taken from online repositories of National Institute of Standards and Technology (NIST), as well as the Sandia National Laboratories, the LAMMPS database. Using molecular dynamic simulations, the three independent elastic constants, C11, C12 and C44 were found for Cu, Al and Ni cubic single crystals. Voigt-Reuss-Hill approximation was then implemented to convert elastic constants of the single crystals into isotropic polycrystalline elastic moduli including Bulk, Shear and Young's modulus as well as Poisson's ratio. Simulation results from massive molecular dynamic were compared with available experimental data in the literature to justify the robustness of each potential for each species. Eventually, accurate interatomic potentials have been recommended for finding each of the elastic properties of the pure species. Exactitude of the elastic properties was found to be sensitive to the choice of the force fields. Those potentials were fitted for a specific compound may not necessarily work accurately for all the existing pure species. read less R. Babicheva et al., “Elastic moduli of nanocrystalline binary Al alloys with Fe, Co, Ti, Mg and Pb alloying elements,” Philosophical Magazine. 2016. link Times cited: 13 Abstract: The paper studies the elastic moduli of nanocrystalline (NC)… read more Abstract: The paper studies the elastic moduli of nanocrystalline (NC) Al and NC binary Al–X alloys (X is Fe, Co, Ti, Mg or Pb) by using molecular dynamics simulations. X atoms in the alloys are either segregated to grain boundaries (GBs) or distributed randomly as in disordered solid solution. At 0 K, the rigidity of the alloys increases with decrease in atomic radii of the alloying elements. An addition of Fe, Co or Ti to the NC Al leads to increase in the Young’s E and shear μ moduli, while an alloying with Pb decreases them. The elastic moduli of the alloys depend on a distribution of the alloying elements. The alloys with the random distribution of Fe or Ti demonstrate larger E and μ than those for the corresponding alloys with GB segregations, while the rigidity of the Al–Co alloy is higher for the case of the GB segregations. The moduli E and μ for polycrystalline aggregates of Al and Al–X alloys with randomly distributed X atoms are estimated based on the elastic constants of corresponding single-crystals according to the Voigt-Reuss-Hill approximation, which neglects the contribution of GBs to the rigidity. The results show that GBs in NC materials noticeably reduce their rigidity. Furthermore, the temperature dependence of μ for the NC Al–X alloys is analyzed. Only the Al–Co alloy with GB segregations shows the decrease in μ to the lowest extent in the temperature range of 0–600 K in comparison with the NC pure Al. read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_826591359508_000
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.	EAM_Dynamo_PunYamakovMishin_2013_NiAlCo__MO_826591359508_000
DOI	10.25950/48e9959e https://doi.org/10.25950/48e9959e https://commons.datacite.org/doi.org/10.25950/48e9959e
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 EAM_Dynamo__MD_120291908751_005
Driver	EAM_Dynamo__MD_120291908751_005
KIM API Version	2.0
Potential Type	eam

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
A The letter grade A was assigned because the normalized error in the computation was 4.28320e-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^0 continuous. This means that the model has continuous energy, but a discontinuous first derivative.	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: Ni

Species: Al

Species: Co

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: Ni

Species: Al

Species: Co

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: Ni

Species: Al

Species: Co

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: Co

Species: Al

Species: Ni

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: Co

Species: Ni

Species: Al

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: Ni

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: Ni

Species: Al

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: Co

Species: Al

Species: Ni

Cubic Crystal Basic Properties Table

Species: Al

Species: Co

Species: Ni

Tests

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	29333
Cohesive energy versus lattice constant curve for bcc Co v004	view	28140
Cohesive energy versus lattice constant curve for bcc Ni v004	view	28439
Cohesive energy versus lattice constant curve for diamond Al v004	view	29780
Cohesive energy versus lattice constant curve for diamond Co v004	view	21851
Cohesive energy versus lattice constant curve for diamond Ni v004	view	30936
Cohesive energy versus lattice constant curve for fcc Al v004	view	35818
Cohesive energy versus lattice constant curve for fcc Co v004	view	22587
Cohesive energy versus lattice constant curve for fcc Ni v004	view	34998
Cohesive energy versus lattice constant curve for sc Al v004	view	21613
Cohesive energy versus lattice constant curve for sc Co v004	view	22558
Cohesive energy versus lattice constant curve for sc Ni v004	view	37458

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	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)
Elastic constants for AlNi in AFLOW crystal prototype A3B2_hP5_164_ad_d at zero temperature and pressure v000		view	164781

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	6366
Elastic constants for bcc Co at zero temperature v006	view	2463
Elastic constants for bcc Ni at zero temperature v006	view	1887
Elastic constants for diamond Co at zero temperature v001	view	7613
Elastic constants for fcc Al at zero temperature v006	view	2303
Elastic constants for fcc Co at zero temperature v006	view	2047
Elastic constants for fcc Ni at zero temperature v006	view	2271
Elastic constants for sc Al at zero temperature v006	view	1791
Elastic constants for sc Co at zero temperature v006	view	4382
Elastic constants for sc Ni at zero temperature v006	view	4510

ElasticConstantsHexagonal__TD_612503193866_004

Elastic constants for hexagonal crystals at zero temperature v004

Creators: Junhao Li
Contributor: jl2922
Publication Year: 2019
DOI: https://doi.org/10.25950/d794c746

Computes the elastic constants for hcp crystals 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 hcp Al at zero temperature v004	view	1974
Elastic constants for hcp Co at zero temperature v004	view	1878
Elastic constants for hcp Ni at zero temperature v004	view	2101

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 AlNi in AFLOW crystal prototype A3B2_hP5_164_ad_d v002	view	52314
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype A3B5_oC16_65_ah_bej v002	view	75221
Equilibrium crystal structure and energy for CoNi in AFLOW crystal prototype A3B_cP4_221_c_a v002	view	82634
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype A3B_oP16_62_cd_c v002	view	103510
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype A4B3_cI112_230_af_g v002	view	1275413
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype A5B2_hP28_194_ahk_ch v002	view	100198
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype A9B2_mP22_14_a4e_e v002	view	185524
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cF4_225_a v002	view	78479
Equilibrium crystal structure and energy for Co in AFLOW crystal prototype A_cF4_225_a v002	view	92835
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_cF4_225_a v002	view	55838
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cI2_229_a v002	view	62826
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_cI2_229_a v002	view	65074
Equilibrium crystal structure and energy for Co in AFLOW crystal prototype A_hP2_194_c v002	view	58573
Equilibrium crystal structure and energy for Ni in AFLOW crystal prototype A_hP2_194_c v002	view	45388
Equilibrium crystal structure and energy for Co in AFLOW crystal prototype A_tI2_139_a v002	view	45631
Equilibrium crystal structure and energy for Co in AFLOW crystal prototype A_tP28_136_f2ij v002	view	71454
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype AB3_cF16_225_a_bc v002	view	79049
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype AB3_cP4_221_a_c v002	view	53530
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype AB3_cP4_221_a_c v002	view	90774
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype AB3_hP8_194_c_h v002	view	89743
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype AB_cP2_221_a_b v002	view	80689
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype AB_cP2_221_a_b v002	view	96075
Equilibrium crystal structure and energy for CoNi in AFLOW crystal prototype AB_cP2_221_a_b v002	view	67626

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 fcc Al v003	view	6614975
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Ni v001	view	12500507
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Al v001	view	20308091
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Ni v001	view	25150570
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Al v001	view	16913885
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Ni v001	view	21323945
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Al v001	view	64856597
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Ni v001	view	80806218

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	2111
Equilibrium zero-temperature lattice constant for bcc Co v007	view	2399
Equilibrium zero-temperature lattice constant for bcc Ni v007	view	3039
Equilibrium zero-temperature lattice constant for diamond Al v007	view	2751
Equilibrium zero-temperature lattice constant for diamond Co v007	view	4766
Equilibrium zero-temperature lattice constant for diamond Ni v007	view	3775
Equilibrium zero-temperature lattice constant for fcc Al v007	view	4862
Equilibrium zero-temperature lattice constant for fcc Co v007	view	4255
Equilibrium zero-temperature lattice constant for fcc Ni v007	view	3135
Equilibrium zero-temperature lattice constant for sc Al v007	view	2463
Equilibrium zero-temperature lattice constant for sc Co v007	view	2879
Equilibrium zero-temperature lattice constant for sc Ni v007	view	3231

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	23527
Equilibrium lattice constants for hcp Co v005	view	33205
Equilibrium lattice constants for hcp Ni v005	view	30308

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	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 Al at 293.15 K under a pressure of 0 MPa v002		view	508743
Linear thermal expansion coefficient of fcc Ni at 293.15 K under a pressure of 0 MPa v002		view	976292

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	55277
Phonon dispersion relations for fcc Ni v004		view	49487

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	7291285
Stacking and twinning fault energies for fcc Ni v002		view	7908417

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	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)
Broken-bond fit of high-symmetry surface energies in fcc Al v004		view	27127
Broken-bond fit of high-symmetry surface energies in fcc Ni v004		view	35764

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 fcc Al	view	405723
Monovacancy formation energy and relaxation volume for fcc Ni	view	532129
Monovacancy formation energy and relaxation volume for hcp Co	view	475147

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 fcc Al	view	1097093
Vacancy formation and migration energy for fcc Ni	view	1494349
Vacancy formation and migration energy for hcp Co	view	3594521

Errors

ElasticConstantsCubic__TD_011862047401_006

Test	Error Categories	Link to Error page
Elastic constants for diamond Al at zero temperature v001	other	view
Elastic constants for diamond Ni at zero temperature v001	other	view

EquilibriumCrystalStructure__TD_457028483760_000

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype A4B3_cI112_230_af_g v000	other	view
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype A9B2_mP22_14_a4e_e v000	other	view

EquilibriumCrystalStructure__TD_457028483760_002

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for AlCo in AFLOW crystal prototype A13B4_oP102_31_17a11b_8a2b v002	other	view
Equilibrium crystal structure and energy for Co in AFLOW crystal prototype A_oC4_63_c v002	other	view
Equilibrium crystal structure and energy for AlNi in AFLOW crystal prototype AB3_tP4_123_a_ce v002	other	view

LatticeConstantCubicEnergy__TD_475411767977_006

Test	Error Categories	Link to Error page
Equilibrium zero-temperature lattice constant for diamond Al	other	view
Equilibrium zero-temperature lattice constant for diamond Co	other	view

No Driver

Verification Check	Error Categories	Link to Error page
DimerContinuityC1__VC_303890932454_005	other	view
MemoryLeak__VC_561022993723_004	other	view

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Mishin_updated-Ni-Al-Co-2013.eam.alloy
kimprovenance.edn
kimspec.edn

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EAM_Dynamo__MD_120291908751_005.txz	Tar+XZ	Linux and OS X archive
EAM_Dynamo__MD_120291908751_005.zip	Zip	Windows archive

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EAM_Dynamo_PunYamakovMishin_2013_NiAlCo__MO_826591359508_000

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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