LAMMPS ADP potential for the Mg-H system developed by Smirnova, Starikov and Vlasova (2018) v000

Daria Smirnova; Sergey Starikov; A. M. Vlasova

doi:10.25950/3ae88c4b

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Sim_LAMMPS_ADP_SmirnovaStarikovVlasova_2018_MgH__SM_899925688973_000

Interatomic potential for Hydrogen (H), Magnesium (Mg).
Use this Potential

Title A single sentence description.	LAMMPS ADP potential for the Mg-H system developed by Smirnova, Starikov and Vlasova (2018) v000
Description	This is an interatomic potential intended for the study of Mg-H system using atomistic methods. The reported potential has an angular-dependent form and can be used for simulation of pure magnesium, as well as for consideration of binary cases including Mg and H. The primary purpose of the potential is the simulation of hydrogen behavior in magnesium.
Species The supported atomic species.	H, Mg
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	https://www.ctcms.nist.gov/potentials/entry/2018--Smirnova-D-E-Starikov-S-V-Vlasova-A-M--Mg-H/

Contributor	I Nikiforov
Maintainer	I Nikiforov
Developer	Daria Smirnova Sergey Starikov A. M. Vlasova
Published on KIM	2022
How to Cite	This Simulator Model originally published in [1] is archived in OpenKIM [2-4]. [1] Smirnova DE, Starikov SV, Vlasova AM. New interatomic potential for simulation of pure magnesium and magnesium hydrides. Computational Materials Science [Internet]. 2018;154:295–302. Available from: https://www.sciencedirect.com/science/article/pii/S0927025618304865 doi:10.1016/j.commatsci.2018.07.051 — (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] Smirnova D, Starikov S, Vlasova AM. LAMMPS ADP potential for the Mg-H system developed by Smirnova, Starikov and Vlasova (2018) v000. OpenKIM; 2022. doi:10.25950/3ae88c4b [3] 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 [4] 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. 14 Citations (13 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 . A. Vlasova, “Simulation of Uniaxial Deformation of Magnesium Nanocrystals of ‘Rigid’ and ‘Soft’ Orientations,” Physics of the Solid State. 2020. link Times cited: 1 D. Liang et al., “Preparation of high purity magnesium by vacuum gasification-directional condensation technology,” Separation and Purification Technology. 2023. link Times cited: 0 E. Ibrahim, Y. Lysogorskiy, M. Mrovec, and R. Drautz, “Atomic cluster expansion for a general-purpose interatomic potential of magnesium,” Physical Review Materials. 2023. link Times cited: 2 Abstract: We present a general-purpose parameterization of the atomic … read more Abstract: We present a general-purpose parameterization of the atomic cluster expansion (ACE) for magnesium. The ACE shows outstanding transferability over a broad range of atomic environments and captures physical properties of bulk as well as defective Mg phases in excellent agreement with reference first-principles calculations. We demonstrate the computational efficiency and the predictive power of ACE by calculating properties of extended defects and by evaluating the P-T phase diagram covering temperatures up to 3000 K and pressures up to 80 GPa. We compare the ACE predictions with those of other interatomic potentials, including the embedded-atom method, an angular-dependent potential, and a recently developed neural network potential. The comparison reveals that ACE is the only model that is able to predict correctly the phase diagram in close agreement with experimental observations. read less S. Zhang et al., “Investigating the Impact of Displacement Cascades on Tritium Diffusion in MgT2: A Molecular Dynamics Study,” Materials. 2023. link Times cited: 0 Abstract: Molecular dynamics methods were utilized to investigate disp… read more Abstract: Molecular dynamics methods were utilized to investigate displacement cascades and tritium diffusion in α-MgT2. It was observed from collision cascades results that the stable number of defects weakly depended on temperature, while the peak and stable number of defects linearly increased with increasing the primary knock-on atom energy. The results of the mean square displacement study revealed that defects had a significant impact on tritium diffusion. The clustering of magnesium self-interstitial atoms and diffusing tritium atoms results in an increased diffusion barrier, whereas the formation of clusters between tritium interstitial atoms is relatively difficult and has no significant impact on the diffusion barrier. The presence of magnesium and tritium vacancies has a minimal effect on the diffusion barrier due to the large number of diffusing tritium atoms that offset the adsorption of vacancies on diffusing atoms. Both magnesium and tritium interstitial atoms increase the collision probability of diffusing atoms, leading to an increased diffusion prefactor. Magnesium vacancies cause significant lattice distortion, increasing the diffusion barrier, while the impact of tritium vacancies on the diffusion barrier is small due to their minimal lattice distortion effect. The research uncovered significant disparities in the diffusion properties of hydrogen and tritium, indicating that the results of the study of hydrogen storage could not be applied to tritium. read less T. Pittie, G. Kunwar, S. Das, J. Jain, and K. N. M. Anoop, “Determining the threshold displacement energy of magnesium using molecular dynamics simulations,” Bulletin of Materials Science. 2022. link Times cited: 1 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 A. Mayer, “Micromechanical model of nanoparticle compaction and shock waves in metal powders,” International Journal of Plasticity. 2021. link Times cited: 11 S. Starikov et al., “Angular-dependent interatomic potential for large-scale atomistic simulation of iron: Development and comprehensive comparison with existing interatomic models,” Physical Review Materials. 2021. link Times cited: 16 Abstract: The development of classical interatomic potential for iron … read more Abstract: The development of classical interatomic potential for iron is a quite demanding task with a long history background. A new interatomic potential for simulation of iron was created with a focus on description of crystal defects properties. In contrast with previous studies, here the potential development was based on force-matching method that requires only ab initio data as reference values. To verify our model, we studied various features of body-centered-cubic iron including the properties of point defects (vacancy and self-interstitial atom), the Peierls energy barrier for dislocations (screw and mix types), and the formation energies of planar defects (surfaces, grain boundaries, and stacking fault). The verification also implies thorough comparison of a potential with 11 other interatomic potentials reported in literature. This potential correctly reproduces the largest number of iron characteristics which ensures its advantage and wider applicability range compared to the other considered classical potentials. Here application of the model is illustrated by estimation of self-diffusion coefficients and the calculation of fcc lattice properties at high temperature. read less N. Wang and S. Huang, “Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential,” Physical Review B. 2020. link Times cited: 3 Abstract: We introduce a machine-learning (ML) interatomic potential f… read more Abstract: We introduce a machine-learning (ML) interatomic potential for Mg-H system based on Behler-Parrinello approach. In order to fit the complex bonding conditions in the cluster structure, we combine multiple sampling strategies to obtain training samples that contain a variety of local atomic environments. First-principles calculations based on density functional theory (DFT) are employed to get reference energies and forces for training the ML potential. For the calculation of bulk properties, phonon dispersion, gas-phase ${\mathrm{H}}_{2}$ interactions, and the potential energy surface (PES) for ${\mathrm{H}}_{2}$ dissociative adsorption on Mg(0001) surfaces, our ML potential has reached DFT accuracy at the level of GGA-PBE, and can be extended by combining the DFT-D3 method to describe van der Waals interaction. Moreover, through molecular dynamics (MD) simulations based on the ML potential, we find that for ${\mathrm{Mg}}_{n}{\mathrm{H}}_{m}$ clusters, $\mathrm{Mg}/\mathrm{Mg}{\mathrm{H}}_{x}$ phase separation occurs when $ml2n$, and for a cluster with a diameter of about 4 nm, the Mg part of the cluster forms a hexagonal close-packed (hcp) nanocrystalline structure at low temperature. Also, the calculated diffusion coefficients reproduce the experimental values and confirm an Arrhenius type temperature dependence in the range of 400 to 700 K. read less T. Tang et al., “A polycrystal plasticity based thermo-mechanical-dynamic recrystallization coupled modeling method and its application to light weight alloys,” International Journal of Plasticity. 2019. link Times cited: 34 Y. Chang et al., “Ti and its alloys as examples of cryogenic focused ion beam milling of environmentally-sensitive materials,” Nature Communications. 2019. link Times cited: 88 A. Vlasova, “Deformation features of magnesium [11̅01]- and [0001]-nanocrystal with hydrogen and vacancies.” 2018. link Times cited: 0 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 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
Funding	Award Number: 16-33-60027 Funder: Russian Foundation for Basic Research Award Number: 859480 Funder: K2

Short KIM ID The unique KIM identifier code.	SM_899925688973_000
Extended KIM ID The long form of the KIM ID including a human readable prefix (100 characters max), two underscores, and the Short KIM ID. Extended KIM IDs can only contain alpha-numeric characters (letters and digits) and underscores and must begin with a letter.	Sim_LAMMPS_ADP_SmirnovaStarikovVlasova_2018_MgH__SM_899925688973_000
DOI	10.25950/3ae88c4b https://doi.org/10.25950/3ae88c4b https://commons.datacite.org/doi.org/10.25950/3ae88c4b
KIM Item Type	Simulator Model
KIM API Version	2.2
Simulator Name The name of the simulator as defined in kimspec.edn.	LAMMPS
Potential Type	adp
Simulator Potential	adp
Run Compatibility	portable-models

Verification Check Dashboard

(Click here to learn more about Verification Checks)

Grade	Name	Category	Brief Description	Full Results	Aux File(s)
P All elements claimed by model are supported in at least one of the tested configurations.	vc-species-supported-as-stated	mandatory	The model supports all species it claims to support; see full description.	Results	Files
P Periodic boundary conditions were correctly supported for all configurations that the model was able to compute.	vc-periodicity-support	mandatory	Periodic boundary conditions are handled correctly; see full description.	Results	Files
N/A Unable to perform verification check due to an error.	vc-permutation-symmetry	mandatory	Total energy and forces are unchanged when swapping atoms of the same species; see full description.	Results	Files
B The letter grade B was assigned because the normalized error in the computation was 1.13308e-06 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
F 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
N/A Thread safety can only be checked for KIM Models.	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

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: H

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

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: Mg

Species: H

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: H

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: H

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: Mg

Species: H

Cubic Crystal Basic Properties Table

Species: H

Species: Mg

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	3481
Cohesive energy versus lattice constant curve for diamond Mg v004	view	3382
Cohesive energy versus lattice constant curve for fcc Mg v004	view	4123
Cohesive energy versus lattice constant curve for sc Mg v004	view	3976

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 HMg in AFLOW crystal prototype A2B_cF12_225_c_a at zero temperature and pressure v000		view	190908
Elastic constants for HMg in AFLOW crystal prototype A2B_cP12_205_c_a at zero temperature and pressure v000		view	2838659

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 H at zero temperature v006	view	24596
Elastic constants for bcc Mg at zero temperature v006	view	22647
Elastic constants for diamond H at zero temperature v001	view	62302
Elastic constants for diamond Mg at zero temperature v001	view	62083
Elastic constants for fcc H at zero temperature v006	view	13455
Elastic constants for fcc Mg at zero temperature v006	view	13381
Elastic constants for sc H at zero temperature v006	view	16511
Elastic constants for sc Mg at zero temperature v006	view	43554

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 HMg in AFLOW crystal prototype A2B_cF12_225_c_a v002	view	84578
Equilibrium crystal structure and energy for HMg in AFLOW crystal prototype A2B_cP12_205_c_a v002	view	116173
Equilibrium crystal structure and energy for HMg in AFLOW crystal prototype A2B_oP12_60_d_c v002	view	96811
Equilibrium crystal structure and energy for HMg in AFLOW crystal prototype A2B_oP12_62_2c_c v002	view	100271
Equilibrium crystal structure and energy for HMg in AFLOW crystal prototype A2B_oP24_61_2c_c v002	view	171757
Equilibrium crystal structure and energy for HMg in AFLOW crystal prototype A2B_tP6_136_f_a v002	view	71265
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cF4_225_a v002	view	83854
Equilibrium crystal structure and energy for H in AFLOW crystal prototype A_cI2_229_a v002	view	95044
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_cI2_229_a v002	view	72663
Equilibrium crystal structure and energy for H in AFLOW crystal prototype A_hP2_194_c v002	view	268715
Equilibrium crystal structure and energy for Mg in AFLOW crystal prototype A_hP2_194_c v002	view	44233
Equilibrium crystal structure and energy for H in AFLOW crystal prototype A_hP4_194_f v002	view	135167
Equilibrium crystal structure and energy for H in AFLOW crystal prototype A_tP1_123_a v002	view	77302

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 H v007	view	30563
Equilibrium zero-temperature lattice constant for bcc Mg v007	view	24674
Equilibrium zero-temperature lattice constant for diamond H v007	view	27739
Equilibrium zero-temperature lattice constant for diamond Mg v007	view	28289
Equilibrium zero-temperature lattice constant for fcc H v007	view	27283
Equilibrium zero-temperature lattice constant for fcc Mg v007	view	35408
Equilibrium zero-temperature lattice constant for sc H v007	view	28103
Equilibrium zero-temperature lattice constant for sc Mg v007	view	22816

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	660199

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	5174638

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	3170467

Errors

ElasticConstantsHexagonal__TD_612503193866_004

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

LatticeConstantHexagonalEnergy__TD_942334626465_005

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

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Verification Check	Error Categories	Link to Error page
MemoryLeak__VC_561022993723_004	other	view

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CMakeLists.txt
LICENSE
Mg_H.adp.alloy.txt
kimprovenance.edn
kimspec.edn
smspec.edn

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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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