MEAM Potential for the Mg-Zn system developed by Jang et al. (2018) v002

Hyo-Sun Jang; Kyeong-Min Kim; Byeong-Joo Lee

doi:10.25950/2b8ee853

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MEAM_LAMMPS_JangKimLee_2018_ZnMg__MO_474962707676_002

Interatomic potential for Magnesium (Mg), Zinc (Zn).
Use this Potential

Title A single sentence description.	MEAM Potential for the Mg-Zn system developed by Jang 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.	Interatomic potentials for Mg–Zn binary system has been developed on the basis of the second nearest-neighbor modified embedded-atom method formalism. The potential reproduce the alloy behavior (thermodynamic, structural, and elastic properties of compounds and solution phases) of Mg-Zn alloys well in good agreement with experiments, first-principles and CALPHAD. In the original paper (Jang et al, Calphad, 60, 2018), the applicability of the developed potentials to atom-scale investigations on the slip behavior of Mg-Zn alloys is also demonstrated by showing that the calculated effects of Zn on the general stacking fault energy on the basal, prismatic and pyramidal planes are consistent with first-principles calculations.
Species The supported atomic species.	Mg, Zn
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	http://cmse.postech.ac.kr/home_2nnmeam

Contributor	Hyeon-Seok Do
Maintainer	Hyeon-Seok Do
Developer	Hyo-Sun Jang Kyeong-Min Kim Byeong-Joo Lee
Published on KIM	2023
How to Cite	This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Jang H-S, Kim K-M, Lee B-J. Modified embedded-atom method interatomic potentials for pure Zn and Mg-Zn binary system. Calphad. 2018;60:200–7. doi:10.1016/j.calphad.2018.01.003 — (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] Jang H-S, Kim K-M, Lee B-J. MEAM Potential for the Mg-Zn system developed by Jang et al. (2018) v002. OpenKIM; 2023. doi:10.25950/2b8ee853 [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. 23 Citations (11 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 . P. Yi, T. Sasaki, S. Prameela, T. Weihs, and M. Falk, “The Interplay between Solute Atoms and Vacancy Clusters in Magnesium Alloys,” SSRN Electronic Journal. 2021. link Times cited: 2 Abstract: Atomic-scale calculations indicate that both stress effects … read more Abstract: Atomic-scale calculations indicate that both stress effects and chemical binding contribute to the redistribution of solute in the presence of vacancy clusters in magnesium alloys. As the size of the vacancy cluster increases, chemical binding becomes more important relative to stress. By affecting the diffusivity of vacancies and vacancy clusters, solute atoms facilitate clustering and stabilize the resulting vacancy clusters, increasing their potential to promote solute segregation and to serve as heterogeneous nucleation sites during precipitation. Experimental observation of solute segregation in simultaneously deformed and aged Mg-Al alloys provides support for this mechanism. read less J.-K. Lee and B.-J. Lee, “The Origin of Activation of Non-basal Slip in Mg-Ce Dilute Alloy: An Atomistic Simulation Study,” Metallurgical and Materials Transactions A. 2021. link Times cited: 9 X. Gao, N. Li, Z. Song, K. Wu, Y. Cheng, and B.-yang Xiao, “Atomic structure evolution and linear regression fitting models for pre-breakdown electric field strength of FCC, BCC and HCP metal nano-emitters under high electric field from PIC-ED–MD simulations,” Journal of Physics D: Applied Physics. 2023. link Times cited: 1 Abstract: Multi-scale and multi-physics simulations are carried out fo… read more Abstract: Multi-scale and multi-physics simulations are carried out for nano-emitters consisting of FCC (Al, Cu and Au), BCC (V, Mo and W) and HCP (Ti, Zn and Zr) metals, using hybrid electrodynamics coupled with molecular dynamics-particle in cell simulations (PIC-ED–MD). We show that the tilting of the nano-emitter at low temperature and small electric field (E-field) is mainly caused either by the movement of partial dislocations at the apex of the nanotip or by the elastic local distortions of atomic registries away from their ideal lattice sites (FCC/BCC/HCP). At high E-field, the intense resistive heating due to the strong electron emission leads to the direct melting of the apex of nano-emitters. For nano-emitters consisting of low melting point metals such as Al, Zn and Au, the thermal runaway is driven by the elongation, thinning and necking of the molten region. Meanwhile, the elongation, thinning and sharpening produce the nano-protrusion at the apex of metal nano-emitters, and the detachment of atoms or atomic clusters from the nano-protrusion mainly contributes to the thermal runaway event for refractor metals such as Ti, Zr, Mo and W. The critical E-field strength of metal nano-emitters is found to be strongly correlated with structural parameters (atomic coordination number of liquid and equilibrium lattice constant), thermodynamic quantities (cohesive energy and enthalpy of evaporation) and phase transition temperatures (melting point and boiling point). These correlations enable us to establish either single-variable linear fitting models or multi-variable linear regression models to predict the critical E-field value for metal nano-emitters with good credibility. read less S. Zhang, L. Sun, R. Song, and C. Cai, “Wetting characteristics of Zn on Fe Surfaces and its Influencing Factors: Molecular Dynamics Simulation,” Surfaces and Interfaces. 2023. link Times cited: 2 Y. Li et al., “Deposition of Horizontally Stacked Zn Crystals on Single Layer 1T‐VSe2 for Dendrite‐Free Zn Metal Anodes,” Advanced Energy Materials. 2022. link Times cited: 6 Abstract: Owing to the moderate redox potential and high safety, Zn me… read more Abstract: Owing to the moderate redox potential and high safety, Zn metal anodes have been garnering great attention. However, the poor reversibility and limited‐service period caused by side reactions and dendrites hinder their applications. Here, a novel anode material consisting of a hexagonal 1T‐Vanadium diselenide (1T‐VSe2) film on graphene is developed as a zincophilic template to epitaxially electrodeposit hexagonal closest packed Zn to replace the conventional metal substrates in Zn batteries. The 1T‐VSe2/Zn anode induces a horizontally (002)‐oriented plate‐like Zn crystal deposition morphology instead of randomly oriented grains that prompts the compact Zn deposition. According to density functional theory calculations, the VSe2 substrate exhibits a higher Zn adsorption (−0.54 eV) than the graphene (−0.38 eV) or neat Zn (−0.48 eV) counterparts, leading to the enhanced zincophilicity and a lower nucleation overpotential, in agreement with the experimental results. The force field‐based molecular dynamics simulations visualize Zn nucleation and morphological evolution at the atomistic level. The rapid adatom diffusion on VSe2 leads to layer‐by‐layer Zn electrodeposits with higher fraction of the (002) facets to effectively prohibit dendrite formation. The symmetric cell with 1T‐VSe2/Zn delivers an ultra‐stable cyclic life of 2500 h with 50 mV overpotential at 1 mA cm−2 and 1 mAh cm−2. read less H.-S. Jang, J.-K. Lee, A. J. Tapia, N. Kim, and B.-J. Lee, “Activation of non-basal slip in multicomponent Mg alloys,” Journal of Magnesium and Alloys. 2021. link Times cited: 23 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 R. Boom and F. D. Boer, “Enthalpy of formation of binary solid and liquid Mg alloys – Comparison of Miedema-model calculations with data reported in literature,” Calphad-computer Coupling of Phase Diagrams and Thermochemistry. 2020. link Times cited: 4 H.-S. Jang, D. Seol, and B.-J. Lee, “Modified embedded-atom method interatomic potential for the Mg–Zn–Ca ternary system,” Calphad-computer Coupling of Phase Diagrams and Thermochemistry. 2019. link Times cited: 6 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.-S. Jang and B.-J. Lee, “Effects of Zn on 〈c + a〉 slip and grain boundary segregation of Mg alloys,” Scripta Materialia. 2019. link Times cited: 44 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 H. Zhou, D. Dickel, and C. D. Barrett, “Improving stability and transferability of machine learned interatomic potentials using physically informed bounding potentials,” Journal of Materials Research. 2023. link Times cited: 1 N. Gadwala, N. Kumar, and D. Ravinder, “Rare Earth Gd3+-Substituted BiFeO3 Perovskite Nanoparticles: Synthesis, Structural, Optical, and Magnetic Properties,” Brazilian Journal of Physics. 2023. link Times cited: 1 B. Yao, Z. R. Liu, D. Legut, and R. F. Zhang, “Hybrid potential model with high feasibility and flexibility for metallic and covalent solids,” Physical Review B. 2023. link Times cited: 0 M. S. Nitol, D. Dickel, and C. Barrett, “Artificial neural network potential for pure zinc,” Computational Materials Science. 2021. link Times cited: 11 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 W. Jian, D. Hui, and D. Lau, “Nanoengineering in biomedicine: Current development and future perspectives,” Nanotechnology Reviews. 2020. link Times cited: 35 Abstract: Recent advances in biomedicine largely rely on the developme… read more Abstract: Recent advances in biomedicine largely rely on the development in nanoengineering. As the access to unique properties in biomaterials is not readily available from traditional techniques, the nanoengineering becomes an effective approach for research and development, by which the performance as well as the functionalities of biomaterials has been greatly improved and enriched. This review focuses on the main materials used in biomedicine, including metallic materials, polymers, and nanocomposites, as well as the major applications of nanoengineering in developing biomedical treatments and techniques. Research that provides an in-depth understanding of material properties and efficient enhancement of material performance using molecular dynamics simulations from the nanoengineering perspective are discussed. The advanced techniques which facilitate nanoengineering in biomedical applications are also presented to inspire further improvement in the future. Furthermore, the potential challenges of nanoengineering in biomedicine are evaluated by summarizing concerned issues and possible solutions. Graphical abstract read less M. Wu, R. Pan, J. Liang, G. Zhou, L. Ma, and C. Zhang, “Structural, Elastic and Electronic Properties of γ˝ Phase Precipitate in Mg-Gd-Zn Alloy,” Journal of the chemical society of pakistan. 2019. link Times cited: 0 Abstract: The γ˝ phase (Mg4GdZn) precipitate in Mg-Gd-Zn alloy was cal… read more Abstract: The γ˝ phase (Mg4GdZn) precipitate in Mg-Gd-Zn alloy was calculated via ﬁrst-principle density functional theory within the generalized gradient approximation. Through structure optimization of full relaxation, the lattice parameters were theoretically obtained, and the calculated Mg4GdZn is the most energetically stable in view of the formation energy. Independent elastic constants were also calculated, illustrating the calculated Mg4GdZn is mechanically stable. The shear modulus, polycrystalline bulk modulus, Poisson ratio, and Young’s modulus of Mg4GdZn were calculated via the Voigt-Reuss-Hill approximation. Elastic anisotropy and ductility were analyzed in details. Seen from their charge density distribution and electronic density of states, both metallic bond and covalent bond were found in Mg4GdZn. read less M. Zacate, “Modified embedded-atom method potential for cadmium,” Hyperfine Interactions. 2019. link Times cited: 0 D. Dickel, M. Baskes, I. Aslam, and C. Barrett, “New interatomic potential for Mg–Al–Zn alloys with specific application to dilute Mg-based alloys,” Modelling and Simulation in Materials Science and Engineering. 2018. link Times cited: 42 Abstract: Because of its very large c/a ratio, zinc has proven to be a… read more Abstract: Because of its very large c/a ratio, zinc has proven to be a difficult 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. As an alloying element, however, useful zinc potentials can be generated by relaxing the condition that hcp be the lowest energy structure. In this paper, we present a MEAM zinc potential, which gives accurate material properties for the pure state, as well as a MEAM ternary potential for the Mg–Al–Zn system which will allow the atomistic modeling of a wide class of alloys containing zinc. The effects of zinc in simple Mg–Zn for this potential is demonstrated and these results verify the accuracy for the new potential in these systems. read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	MO_474962707676_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_JangKimLee_2018_ZnMg__MO_474962707676_002
DOI	10.25950/2b8ee853 https://doi.org/10.25950/2b8ee853 https://commons.datacite.org/doi.org/10.25950/2b8ee853
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_JangKimLee_2018_ZnMg__MO_474962707676_001

Verification Check Dashboard

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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 2.84758e-09 compared with a machine precision of 2.22045e-16. The letter grade was based on 'score=log10(error/eps)', with ranges A=[0, 7.5], B=(7.5, 10.0], C=(10.0, 12.5], D=(12.5, 15.0), F>15.0. 'A' is the best grade, and 'F' indicates failure.	vc-forces-numerical-derivative	consistency	Forces computed by the model agree with numerical derivatives of the energy; see full description.	Results	Files
F The model is C^-1 continuous. This means that the model has discontinuous energy.	vc-dimer-continuity-c1	informational	The energy versus separation relation of a pair of atoms is C1 continuous (i.e. the function and its first derivative are continuous); see full description.	Results	Files
P Model energy and forces are invariant with respect to rigid-body motion (translation and rotation) for all configurations the model was able to compute.	vc-objectivity	informational	Total energy is unchanged and forces transform correctly under rigid-body translation and rotation; see full description.	Results	Files
P Model energy has inversion symmetry for all configurations the model was able to compute.	vc-inversion-symmetry	informational	Total energy is unchanged and forces change sign when inverting a configuration through the origin; see full description.	Results	Files
P No memory leak detected.	vc-memory-leak	informational	The model code does not have memory leaks (i.e. it releases all allocated memory at the end); see full description.	Results	Files
P All threads give identical results for tested case. Model appears to be thread-safe.	vc-thread-safe	mandatory	The model returns the same energy and forces when computed in serial and when using parallel threads for a set of configurations. Note that this is not a guarantee of thread safety; see full description.	Results	Files
P Unit conversion successful	vc-unit-conversion	mandatory	The model is able to correctly convert its energy and/or forces to different unit sets; see full description.	Results	Files

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

Species: Zn

Species: Mg

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: 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

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: 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: Zn

Species: Mg

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.

(No matching species)

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.

(No matching species)

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: Zn

Species: Mg

Cubic Crystal Basic Properties Table

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 Mg v004	view	5600
Cohesive energy versus lattice constant curve for bcc Zn v004	view	5749
Cohesive energy versus lattice constant curve for diamond Mg v004	view	5610
Cohesive energy versus lattice constant curve for diamond Zn v004	view	6331
Cohesive energy versus lattice constant curve for fcc Mg v004	view	5719
Cohesive energy versus lattice constant curve for fcc Zn v004	view	6111
Cohesive energy versus lattice constant curve for sc Mg v004	view	5742
Cohesive energy versus lattice constant curve for sc Zn v004	view	5102

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 MgZn in AFLOW crystal prototype A2B11_cP39_200_f_begik at zero temperature and pressure v000		view	4254597

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 Mg at zero temperature v006	view	21276
Elastic constants for bcc Zn at zero temperature v006	view	23485
Elastic constants for diamond Mg at zero temperature v001	view	78479
Elastic constants for diamond Zn at zero temperature v001	view	30037
Elastic constants for fcc Mg at zero temperature v006	view	40491
Elastic constants for fcc Zn at zero temperature v006	view	15608
Elastic constants for sc Mg at zero temperature v006	view	48663
Elastic constants for sc Zn at zero temperature v006	view	17833

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 MgZn in AFLOW crystal prototype A2B11_cP39_200_f_begik v002	view	202088
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype A4B7_mC110_12_10i_ae8i4j v002	view	1316850
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cF4_225_a v002	view	89596
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cI2_229_a v002	view	76713
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_hP2_194_c v002	view	48608
Equilibrium crystal structure and energy for Zn in AFLOW crystal prototype A_hP2_194_c v002	view	54502
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB2_hP12_194_f_ah v002	view	59666
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB2_oC12_63_c_ac v002	view	55292
Equilibrium crystal structure and energy for MgZn in AFLOW crystal prototype AB5_mC48_12_2i_ac5i2j v002	view	327979

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 Mg v007	view	11706
Equilibrium zero-temperature lattice constant for bcc Zn v007	view	13914
Equilibrium zero-temperature lattice constant for diamond Mg v007	view	11925
Equilibrium zero-temperature lattice constant for diamond Zn v007	view	11319
Equilibrium zero-temperature lattice constant for fcc Mg v007	view	11149
Equilibrium zero-temperature lattice constant for fcc Zn v007	view	10672
Equilibrium zero-temperature lattice constant for sc Mg v007	view	13104
Equilibrium zero-temperature lattice constant for sc Zn v007	view	19289

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	Test Results	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Equilibrium lattice constants for hcp Mg v005		view	170116
Equilibrium lattice constants for hcp Zn v005		view	177437

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	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)
Monovacancy formation energy and relaxation volume for hcp Mg		view	869164
Monovacancy formation energy and relaxation volume for hcp Zn		view	730757

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	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)
Vacancy formation and migration energy for hcp Mg		view	807249
Vacancy formation and migration energy for hcp Zn		view	4693086

Errors

ElasticConstantsHexagonal__TD_612503193866_004

Test	Error Categories	Link to Error page
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 AB_oI48_44_6c_abc2de v002	other	view

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ZnMg.meam
elems.meam
kimprovenance.edn
kimspec.edn
library.meam

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MEAM_LAMMPS__MD_249792265679_002.zip	Zip	Windows archive

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MEAM_LAMMPS_JangKimLee_2018_ZnMg__MO_474962707676_002

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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

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