MEAM potential for Mg–Al–Zn alloys developed by Dickel et al. (2018) v002

Doyl E. Dickel; Michael I. Baskes; Imran Aslam; Christopher D. Barrett

doi:10.25950/ca972fcb

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MEAM_LAMMPS_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_002

Interatomic potential for Aluminum (Al), Magnesium (Mg), Zinc (Zn).
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Title A single sentence description.	MEAM potential for Mg–Al–Zn alloys developed by Dickel et al. (2018) 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.	Because of its substantial c/a ratio, Zn has proven to be a problematic element to model using semi-empirical classical potentials. It has been shown, in particular, that for the modified embedded atom method (MEAM), a potential cannot simultaneously have an hcp ground state and c/a ratio greater than ideal. However, as an alloying element, practical Zn potentials can be generated by relaxing the condition that hcp be the lowest energy structure. A MEAM Zn potential is developed, which gives accurate material properties for the pure state and a MEAM ternary potential for the Mg-Al–Zn system, which will allow the atomistic modeling of a broad class of alloys containing Zn. Zn's effects in simple Mg–Zn results for this potential verifies the accuracy for the new potential in these systems.
Species The supported atomic species.	Al, Mg, Zn
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	Files are provided by Doyl E Dickel (Mississippi State University) on Mar 6, 2021, and posted with his permission.

Contributor	Yaser Afshar
Maintainer	Yaser Afshar
Developer	Doyl E. Dickel Michael I. Baskes Imran Aslam Christopher D. Barrett
Published on KIM	2023
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Dickel DE, Baskes MI, Aslam I, Barrett CD. New interatomic potential for Mg–Al–Zn alloys with specific application to dilute Mg-based alloys. Modelling and Simulation in Materials Science and Engineering. 2018Apr;26(4):045010. doi:10.1088/1361-651x/aabaad — (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] Dickel DE, Baskes MI, Aslam I, Barrett CD. MEAM potential for Mg–Al–Zn alloys developed by Dickel et al. (2018) v002. OpenKIM; 2023. doi:10.25950/ca972fcb [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. 37 Citations (24 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 . G. Esteban-Manzanares, R. Alizadeh, I. Papadimitriou, D. Dickel, C. Barrett, and J. Llorca, “Atomistic simulations of the interaction of basal dislocations with MgZn2 precipitates in Mg alloys,” Materials Science and Engineering: A. 2020. link Times cited: 14 S. Subedi, L. Morrissey, S. M. Handrigan, and S. Nakhla, “The effect of many-body potential type and parameterisation on the accuracy of predicting mechanical properties of aluminium using molecular dynamics,” Molecular Simulation. 2020. link Times cited: 8 Abstract: ABSTRACT As opposed to traditional laboratory testing, Molec… read more Abstract: ABSTRACT As opposed to traditional laboratory testing, Molecular Dynamics (MD) offers an atomistic scale method to estimate the mechanical properties of metals. However, there is limited literature that shows the effect of interatomic potentials when determining mechanical properties. Hence, the present research was conducted to investigate the accuracy of various interatomic potentials in estimating mechanical properties of aluminium. Several types of potentials, including Embedded Atom Method (EAM), Modified EAM (MEAM) and Reactive Force Field (ReaxFF) were compared with available experimental data for pure aluminium to determine the most accurate interatomic potential. A uniaxial tensile test was performed at room temperature using MD simulations for nanoscale aluminium. Results demonstrated that those potentials parameterised with elastic constants at physically realisable temperatures were consistently more accurate. Overall, the Mishin et al. EAM potential was the most accurate when compared to single-crystal experimental values. Regardless of the potential type, the error was significantly higher for those potentials that did not consider elastic constants during development. In brief, the application of the interatomic potentials to estimate mechanical properties of a nanoscale aluminium was investigated. read less 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 J. Peng, J. Li, B. Liu, Q. Fang, and P. K. Liaw, “Origin of thermal deformation induced crystallization and microstructure formation in additive manufactured FCC, BCC, HCP metals and its alloys,” International Journal of Plasticity. 2023. link Times cited: 0 Y. Pu et al., “Formation and fracture of Mg88Al6Zn6 MGs analyzed by topologically close-packed cluster correlation,” Journal of Materials Research and Technology. 2023. link Times cited: 0 Y. Sun and Y.-gui Chen, “Atomistic modeling of Mg-Al-Zn solid–liquid interfacial free energy,” Computational Materials Science. 2023. link Times cited: 0 M. Motamedi, M. H. Nikzad, and M. R. Nasri, “e Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study,” Materials Today Communications. 2023. link Times cited: 0 X. Zhang, J. Li, Y. Shi, Q. Chen, and H. Yue, “Microstructure evolution of AZ80 magnesium alloy in semi-solid compression by molecular dynamics simulation,” Journal of Materials Research and Technology. 2023. link Times cited: 0 G. Zhang, S. Zhang, R. Song, and C. Cai, “Effect of Mg and Si Contents on Hot-Dip 55Al-Zn Plating: Experimental and Molecular Dynamics Simulation,” Materials Today Communications. 2023. link Times cited: 1 T. Wen et al., “Modelling of dislocations, twins and crack-tips in HCP and BCC Ti,” International Journal of Plasticity. 2023. link Times cited: 4 S. Zhang, R. Song, C. Cai, and S. Zhao, “Diffusion and reaction mechanism in initial stage of Zn–Al–Mg hot-dip coating: molecular dynamics simulation,” Journal of Materials Science. 2023. link Times cited: 1 X. Pan et al., “Microstructure and residual stress modulation of 7075 aluminum alloy for improving fatigue performance by laser shock peening,” International Journal of Machine Tools and Manufacture. 2023. link Times cited: 22 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 A. Clément and T. Auger, “An EAM potential for α-brass copper–zinc alloys: application to plasticity and fracture,” Modelling and Simulation in Materials Science and Engineering. 2022. link Times cited: 1 Abstract: An embedded atom method potential has been developed for cop… read more Abstract: An embedded atom method potential has been developed for copper–zinc alloys valid from 0% to 37% zinc content (dedicated to describe the α fcc phase). It has been fit to a set of first-principles data for the fcc copper, the fcc Cu3Zn DO 23 phase and Zn on a fcc lattice. Elastic anisotropies, the lattice parameter, cohesive energy are used as input. Ponctual defects, surface energies, intrinsic stacking fault and phonon spectrum have been computed and compare well with experimental trends. This potential has been used to study dislocation dissociation and dislocation emission at a crack tip up to 30% Zn. Dislocation emission at the crack tip is correctly described compared with recent parametrization including the surface energy. It is found that with alloying, dislocation emission becomes easier following the decrease of the unstable stacking fault energy with Zn concentration, a non-trivial finding. This potential is therefore well suited to carry out basic studies of plasticity and fracture in α-brass alloys. read less D. F. Shi, A. Ma, M. Pérez-Prado, and C. Cepeda-Jiménez, “Activation of second-order pyramidal slip and other secondary mechanisms in solid solution Mg-Zn alloys and their effect on tensile ductility,” Acta Materialia. 2022. link Times cited: 10 B. Cai, J. Li, W. Lai, J.-bo Liu, and B.-xin Liu, “Construction of Al-Mg-Zn Interatomic Potential and the Prediction of Favored Glass Formation Compositions and Associated Driving Forces,” Materials. 2022. link Times cited: 2 Abstract: An interatomic potential is constructed for the ternary Al-M… read more Abstract: An interatomic potential is constructed for the ternary Al-Mg-Zn system under a proposed modified tight-binding scheme, and it is verified to be realistic. Applying this ternary potential, atomistic simulations predict an intrinsic glass formation region in the composition triangle, within which the glassy alloys are more energetically favored in comparison with their solid solution counterparts. Kinetically, the amorphization driving force of each disordered state is derived to correlate the readiness of its glass-forming ability in practice; thus, an optimal stoichiometry region is pinpointed around Al35Mg35Zn30. Furthermore, by monitoring the structural evolution for various (Al50Mg50)1−xZnx (x = 30, 50, and 70 at.%) compositions, the optimized-glass-former Al35Mg35Zn30 is characterized by both the highest degree of icosahedral ordering and the highest phase stability among the investigated compositions. In addition, the icosahedral network in Al35Mg35Zn30 exhibits a much higher cross-linking degree than that in Al25Mg25Zn50. This suggests that there is a certain correlation between the icosahedral ordering and the larger glass-forming ability of Al35Mg35Zn30. Our results have significant implications in clarifying glass formation and hierarchical atomic structures, and in designing new ternary Al-Mg-Zn glassy alloys with high GFA. read less X. Yang, S. Xu, and L. Liu, “The Shock Response and Spall Mechanism of Mg–Al–Zn Alloy: Molecular Dynamics Study,” Acta Mechanica Solida Sinica. 2022. link Times cited: 7 A. Mahata, T. Mukhopadhyay, and M. A. Zaeem, “Modified embedded-atom method interatomic potentials for Al-Cu, Al-Fe and Al-Ni binary alloys: From room temperature to melting point,” Computational Materials Science. 2022. link Times cited: 27 S. Sarmah, N. Kumar, J. Patil, M. Paliwal, and R. S. Ayyagari, “A unified modeling approach and experimental validation to predict the mechanical properties in age hardened magnesium alloys,” Mechanics of Materials. 2022. link Times cited: 2 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. Niewczas and A. Kula, “Formation of polytypes structures in Mg single crystals,” Acta Materialia. 2021. link Times cited: 2 H.-S. Jang, D. Seol, and B.-J. Lee, “Modified embedded-atom method interatomic potentials for Mg–Al–Ca and Mg–Al–Zn ternary systems,” Journal of Magnesium and Alloys. 2020. link Times cited: 28 Y. Liu, H. Yan, and X. Cui, “Underlying Mechanisms of the Electrolyte Structure and Dynamics on the Doped-Anode of Magnesium Batteries Based on the Molecular Dynamics Simulations,” Journal of Electrochemical Energy Conversion and Storage. 2020. link Times cited: 1 Abstract: As a potential energy storage cell, the rechargeable magnesi… read more Abstract: As a potential energy storage cell, the rechargeable magnesium (Mg) battery is limited by poor solid-state diffusion of Mg2+. Hence, the fundamental mechanisms between the electrolyte and the Mg electrode need to be deeply explored. In this work, a doped-Mg electrode/MgCl2 aqueous electrolyte system is constructed to explore the electrolyte structure and transport properties of ions through molecular dynamics simulations. Then, extensive simulations are conducted to study the effect of the doping levels on the electrode/electrolyte interface and ionic diffusivity. According to the number densities of different electrodes (i.e., Mg–Zn, Mg–Al, Mg–Si, and pure Mg), the Mg–Si electrode shows the strongest attraction to the ions in the electrolyte, indicating that the Mg–Si electrode can provide a higher ion storage performance. Moreover, the simulation results also show that the electrode capacitance presents a similar non-monotonic relationship with the increase of potential well depth under different doping ratios. At the doping ratio of 9%, the potential well depth has the strongest impact on the electric double layer (EDL) thickness compared with that of the other two doping ratios. The diffusion coefficient of water molecules weakly depends on the doping ratios and electrode materials. In contrast, the diffusion coefficient of ions varies strongly with the electrode materials, which could change up to 10–30% from its reference value (the diffusion coefficient of the Mg electrode system). This study will potentially provide an understanding of the influences of doped-Mg metal anodes on the structure and transport characteristics of Mg rechargeable batteries. 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 H. Mei, L. Cheng, L. Chen, F. Wang, J. Li, and L. Kong, “Development of machine learning interatomic potential for zinc,” Computational Materials Science. 2024. link Times cited: 0 Y. Ji, C. Dong, L. Chen, K. Xiao, and X. Li, “High-throughput computing for screening the potential alloying elements of a 7xxx aluminum alloy for increasing the alloy resistance to stress corrosion cracking,” Corrosion Science. 2021. link Times cited: 14 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 M. S. Nitol, D. Dickel, and C. Barrett, “Artificial neural network potential for pure zinc,” Computational Materials Science. 2021. link Times cited: 11 T. Yang et al., “Self-accommodated defect structures modifying the growth of Laves phase,” Journal of Materials Science & Technology. 2021. link Times cited: 12 J. F. Troncoso and V. Turlo, “Evaluating the applicability of classical and neural network interatomic potentials for modeling body centered cubic polymorph of magnesium,” Modelling and Simulation in Materials Science and Engineering. 2022. link Times cited: 2 Abstract: Magnesium (Mg) is one of the most abundant metallic elements… read more Abstract: Magnesium (Mg) is one of the most abundant metallic elements in nature and presents attractive mechanical properties in the industry. Particularly, it has a low density and relatively high strength/weight and stiffness/weight ratios, which make it one of the most attractive lightweight metals. However, the huge potential of Mg is restricted by its low ductility, associated with its hexagonal close packed (hcp) structure. This problem can be solved if Mg adopts the body centered cubic (bcc) structure, which is stable at high pressure or in confinement with stiff bcc metals like Nb. Molecular dynamics method is a magnificent tool to study material’s structure and deformation mechanisms at the atomic level, however, requiring accurate interatomic potentials. The majority of the interatomic potentials available in the literature for Mg have only been fitted to the properties of its stable hcp phase. In the present work, we perform systematic study of applicability of currently available Mg potentials to modeling the properties of metastable bcc polymorph of Mg, taking into account cohesive energy curves, elastic constants, stacking fault energies, and phonon dispersion curves. We conclude that the modified embedded atom method (MEAM) potentials are the most suitable for investigating bcc Mg in Mg/Nb nano-composites, while the properties of high-pressure bcc Mg would be better modeled by neural network interatomic potentials after different local atomic environments corresponding to bcc Mg being included into the fitting database. read less T. Wen et al., “Atomistic Modelling of All Dislocations and Twins in HCP and BCC Ti,” SSRN Electronic Journal. 2022. link Times cited: 0 Abstract: Ti exhibits complex plastic deformation controlled by active… read more Abstract: Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or quantitative, despite extensive experimental and simulation studies. Here, we determine all the core and twin interface properties in both HCP and BCC Ti using a Deep Potential (DP) and DFT. We determine the core structures, critical resolved shear stresses and mobilities of,,dislocations in HCP and<111>/2 dislocations in BCC Ti. Theslip consists of slow core migration on pyramidal-I planes and fast migration on prism-planes, and is kinetically limited by cross-slips among them. This behaviour is consistent with"locking-unlocking"phenomena in TEM and is likely an intrinsic property. Large-scale DFT calculations provide a peek at the screwcore and glide behaviour, which is further quantified using DP-Ti. The screwis unstable on pyramidal-II planes. The mixedis nearly sessile on pyramidal-I planes, consistent with observations of long dislocations in this orientation. The edge and mixedare unstable against a pyramidal-to-basal (PB) transition and become sessile at high temperatures, corroborate the difficulties in-axis compression of Ti. Finally, in BCC Ti, the<111>/2 screw has a degenerate core with average glide on {112} planes; the<111>/2 edge and mixed dislocations have non-dissociated cores on {110} planes. This work paints a self-consistent, complete picture on all dislocations in Ti, rationalises previous experimental observations and points to future HRTEM examinations of unusual dislocations such as the mixed and PB transformedcores. read less M. Poul, L. Huber, E. Bitzek, and J. Neugebauer, “Systematic atomic structure datasets for machine learning potentials: Application to defects in magnesium,” Physical Review B. 2022. link Times cited: 3 Abstract: We present a physically motivated strategy for the construct… read more Abstract: We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures, together with deformations of cell shape, size, and atomic positions. The resulting potentials turn out to be unbiased and generically applicable to studies of bulk defects without including any defect structures in the training set or employing any additional Active Learning. Using this approach we construct transferable potentials for pure Magnesium that reproduce the properties of hexagonal closed packed (hcp) and body centered cubic (bcc) polymorphs very well. In the process we investigate how different types of training structures impact the properties and the predictive power of the resulting potential. read less H. Wang, Y. Zhang, L. Zhang, and H. Wang, “Crystal Structure Prediction of Binary Alloys via Deep Potential,” Frontiers in Chemistry. 2020. link Times cited: 9 Abstract: Predicting crystal structure has been a challenging problem … read more Abstract: Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg12Al8 shows excellent ductility and Mg5Al27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced. read less D. R. Pratt, L. Morrissey, and S. Nakhla, “Molecular dynamics simulations of nanoindentation – the importance of force field choice on the predicted elastic modulus of FCC aluminum,” Molecular Simulation. 2020. link Times cited: 5 Abstract: ABSTRACT Molecular Dynamics (MD) was used to determine the a… read more Abstract: ABSTRACT Molecular Dynamics (MD) was used to determine the accuracy of different force fields on predicting the elastic modulus of single crystal aluminum through nanoindentation tests. In this work, nanoindentation was performed using three different types of force fields (EAM, MEAM and ReaxFF) and the resulting elastic modulus was compared to the value obtained using elastic constants from standard small strain tensile simulations. When the predicted modulus of each force field was compared to the modulus via elastic constants, the ReaxFF resultant moduli were similar to that of nanoindentation, but for EAM and MEAM the two methods produced significantly different values. Therefore, even if a force field is parameterised for elastic modulus, it does not guarantee the force field will accurately predict the modulus from other procedures. As well, two different methods for calculating modulus from indentation curves were compared: The Hertz approximation and the Oliver and Pharr (O&P) method. For EAM and MEAM force fields, the Hertz method significantly under predicted modulus while the O&P method was in better agreement with the experimental modulus. read less W. Y. Huen, H.-S. Lee, V. Vimonsatit, P. Mendis, and H.-seung Lee, “Transversely isotropic elastic-plastic properties in thermal arc sprayed Al–Zn coating: a microporomechanics approach,” Scientific Reports. 2020. link Times cited: 3 M. Alidoust, D. Kleiven, and J. Akola, “Density functional simulations of pressurized Mg-Zn and Al-Zn alloys,” Physical Review Materials. 2020. link Times cited: 5 Abstract: The Mg-Zn and Al-Zn binary alloys have been investigated the… read more Abstract: The Mg-Zn and Al-Zn binary alloys have been investigated theoretically under static isotropic pressure. The stable phases of these binaries on both initially hexagonal-close-packed (HCP) and face-centered-cubic (FCC) lattices have been determined by utilizing an iterative approach that uses a configurational cluster expansion method, Monte Carlo search algorithm, and density functional theory (DFT) calculations. Based on 64-atom models, it is shown that the most stable phases of the Mg-Zn binary alloy under ambient condition are MgZn3, Mg19Zn45, MgZn, and Mg34Zn30 for the HCP, and MgZn3 and MgZn for the FCC lattice, whereas the Al-Zn binary is energetically unfavorable throughout the entire composition range for both the HCP and FCC lattices under all conditions. By applying an isotropic pressure in the HCP lattice, Mg19Zn45 turns into an unstable phase at P≈10 GPa, a new stable phase Mg3Zn appears at P&20 GPa, and Mg34Zn30 becomes unstable for P&30 GPa. For FCC lattice, the Mg3Zn phase weakly touches the convex hull at P&20 GPa while the other stable phases remain intact up to ≈120 GPa. Furthermore, making use of the obtained DFT results, bulk modulus has been computed for several compositions up to pressure values of the order of ≈120 GPa. The findings suggest that one can switch between Mg-rich and Zn-rich early-stage clusters simply by applying external pressure. Zn-rich alloys and precipitates are more favorable in terms of stiffness and stability against external deformation. read less S.-J. Sun, S. Ju, C.-C. Yang, K.-C. Chang, and I.-J. Lee, “Effects of Strontium incorporation to Mg-Zn-Ca biodegradable bulk metallic glass investigated by molecular dynamics simulation and density functional theory calculation,” Scientific Reports. 2020. link Times cited: 6
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_093637366498_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_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_002
DOI	10.25950/ca972fcb https://doi.org/10.25950/ca972fcb https://commons.datacite.org/doi.org/10.25950/ca972fcb
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_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_001

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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 3.41217e-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: Al

Species: Mg

Species: Zn

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: Mg

Species: Al

Species: Zn

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: Zn

Species: Mg

Species: Al

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: Al

Species: Zn

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: Mg

Species: Al

Species: Zn

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: 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

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: Mg

Species: Zn

Species: Al

Cubic Crystal Basic Properties Table

Species: Al

Species: Mg

Species: Zn

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	14074
Cohesive energy versus lattice constant curve for bcc Mg v004	view	12383
Cohesive energy versus lattice constant curve for bcc Zn v004	view	13268
Cohesive energy versus lattice constant curve for diamond Al v004	view	14064
Cohesive energy versus lattice constant curve for diamond Mg v004	view	15387
Cohesive energy versus lattice constant curve for diamond Zn v004	view	13517
Cohesive energy versus lattice constant curve for fcc Al v004	view	13328
Cohesive energy versus lattice constant curve for fcc Mg v004	view	14945
Cohesive energy versus lattice constant curve for fcc Zn v004	view	15313
Cohesive energy versus lattice constant curve for sc Al v004	view	14650
Cohesive energy versus lattice constant curve for sc Mg v004	view	13755
Cohesive energy versus lattice constant curve for sc Zn v004	view	15092

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 AlMg in AFLOW crystal prototype A12B17_cI58_217_g_acg at zero temperature and pressure v000	view	823723
Elastic constants for AlMg in AFLOW crystal prototype A14B13_cI54_229_ef_ah at zero temperature and pressure v000	view	1282911
Elastic constants for MgZn in AFLOW crystal prototype A2B11_cP39_200_f_begik at zero temperature and pressure v000	view	2022723

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	39147
Elastic constants for bcc Mg at zero temperature v006	view	45497
Elastic constants for bcc Zn at zero temperature v006	view	44099
Elastic constants for diamond Al at zero temperature v001	view	88639
Elastic constants for diamond Mg at zero temperature v001	view	160419
Elastic constants for diamond Zn at zero temperature v001	view	64123
Elastic constants for fcc Al at zero temperature v006	view	33508
Elastic constants for fcc Mg at zero temperature v006	view	65007
Elastic constants for fcc Zn at zero temperature v006	view	48516
Elastic constants for sc Al at zero temperature v006	view	49326
Elastic constants for sc Zn at zero temperature v006	view	54802

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 AlMg in AFLOW crystal prototype A12B17_cI58_217_g_acg v002	view	158280
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A14B13_cI54_229_ef_ah v002	view	249426
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype A2B11_cP39_200_f_begik v002	view	166422
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A2B_tI24_141_2e_e v002	view	136861
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A30B23_hR53_148_5f_a2c3f v002	view	2567883
Equilibrium crystal structure and energy for AlMgZn in AFLOW crystal prototype A4B19C15_oC152_63_h_c3f2g2h_aefg2h v001	view	1340261
Equilibrium crystal structure and energy for AlMgZn in AFLOW crystal prototype A4B2C3_hP18_194_ef_f_h v001	view	69752
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype A4B7_mC110_12_10i_ae8i4j v002	view	766684
Equilibrium crystal structure and energy for AlMg in AFLOW crystal prototype A67B41_cP108_221_aeh2il_cfgm v002	view	881900
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cF4_225_a v002	view	63859
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cF4_225_a v002	view	87167
Equilibrium crystal structure and energy for Al in AFLOW crystal prototype A_cI2_229_a v002	view	62218
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cI2_229_a v002	view	68233
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_hP2_194_c v002	view	49702
Equilibrium crystal structure and energy for Zn in AFLOW crystal prototype A_hP2_194_c v002	view	59338
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB2_hP12_194_f_ah v002	view	64649
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB5_mC48_12_2i_ac5i2j v002	view	164174

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	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)
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Al v003		view	49975766
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Al v001		view	76862499

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	16049
Equilibrium zero-temperature lattice constant for bcc Mg v007	view	16197
Equilibrium zero-temperature lattice constant for bcc Zn v007	view	17903
Equilibrium zero-temperature lattice constant for diamond Al v007	view	18559
Equilibrium zero-temperature lattice constant for diamond Mg v007	view	18037
Equilibrium zero-temperature lattice constant for diamond Zn v007	view	18738
Equilibrium zero-temperature lattice constant for fcc Al v007	view	26062
Equilibrium zero-temperature lattice constant for fcc Mg v007	view	19509
Equilibrium zero-temperature lattice constant for fcc Zn v007	view	19951
Equilibrium zero-temperature lattice constant for sc Al v007	view	15019
Equilibrium zero-temperature lattice constant for sc Mg v007	view	17067
Equilibrium zero-temperature lattice constant for sc Zn v007	view	18405

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	420656
Equilibrium lattice constants for hcp Mg v005	view	501061
Equilibrium lattice constants for hcp Zn v005	view	501503

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	6093985

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	96973

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	98907375

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	176273

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	930416
Monovacancy formation energy and relaxation volume for hcp Mg	view	834268
Monovacancy formation energy and relaxation volume for hcp Zn	view	1035325

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	2121816
Vacancy formation and migration energy for hcp Mg	view	1119694
Vacancy formation and migration energy for hcp Zn	view	4358775

Errors

ElasticConstantsCubic__TD_011862047401_006

Test	Error Categories	Link to Error page
Elastic constants for sc 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 Mg at zero temperature v004	other	view
Elastic constants for hcp Zn at zero temperature v004	other	view

EquilibriumCrystalStructure__TD_457028483760_002

Test	Error Categories	Link to Error page
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB2_oC12_63_c_ac v002	other	view
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB_oI48_44_6c_abc2de v002	other	view

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

Test	Error Categories	Link to Error page
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 112 symmetric tilt grain boundary in fcc Al v001	other	view

LinearThermalExpansionCoeffCubic__TD_522633393614_001

Test	Error Categories	Link to Error page
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

SurfaceEnergyCubicCrystalBrokenBondFit__TD_955413365818_004

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

Files

CMakeLists.txt
LICENSE
MgAlZn.library.meam
MgAlZn.parameter.meam
elems.meam
kimprovenance.edn
kimspec.edn

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MEAM_LAMMPS__MD_249792265679_002.txz	Tar+XZ	Linux and OS X archive
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MEAM_LAMMPS_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_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

Files

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