LAMMPS Tersoff-ZBL potential for Fe developed by J. Byggmästar and Granberg (2020) v000

Jesper Byggmästar; Fredric Gustaf Granberg

doi:10.25950/436c99e7

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Sim_LAMMPS_TersoffZBL_ByggmastarGranberg_2020_Fe__SM_958863895234_000

Interatomic potential for Iron (Fe).
Use this Potential

Title A single sentence description.	LAMMPS Tersoff-ZBL potential for Fe developed by J. Byggmästar and Granberg (2020) v000
Description	This potential was developed mainly for defect clusters and radiation damage. See also the published paper and its supplementary pdf (open access) for benchmark results. Paper abstract: Density functional theory predicts clusters in the form of the C15 Laves phase to be the most stable cluster of self-interstitials in iron at small sizes. The C15 clusters can form as a result of irradiation, but their prevalence and survival in harsh irradiation conditions have not been thoroughly studied. Using a new bond-order potential optimised for molecular dynamics simulations of radiation damage, we explore the dynamical stability of the C15 clusters in iron under irradiation conditions. We find that small C15 clusters make up 5–20% of the interstitial clusters formed directly in cascades. In continuous irradiation, C15 clusters are frequently formed, after which they remain highly stable and grow by absorbing nearby single interstitial atoms. Growth of C15 clusters ultimately leads to collapse into dislocation loops, most frequently into 1/2 <111> loops and only rarely collapsing into <100> loops at low temperatures. The population, size, and collapse of C15 clusters during continuous irradiation correlates well with their formation energies relative to dislocation loops calculated at zero Kelvin.
Species The supported atomic species.	Fe
Disclaimer A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.	None
Content Origin	https://doi.org/10.1016/j.jnucmat.2019.151893
Content Other Locations	https://doi.org/10.1016/j.jnucmat.2019.151893 https://www.ctcms.nist.gov/potentials/entry/2020--Byggmastar-J-Granberg-F--Fe/2020--Byggmastar-J--Fe--LAMMPS--ipr1.html

Contributor	Jesper Byggmästar
Maintainer	Jesper Byggmästar
Developer	Jesper Byggmästar Fredric Gustaf Granberg
Published on KIM	2020
How to Cite	This Simulator Model originally published in [1] is archived in OpenKIM [2-4]. [1] Byggmästar J, Granberg F. Dynamical Stability of Radiation-Induced C15 Clusters in Iron. Journal of Nuclear Materials. 2020Jan;528:151893. doi:10.1016/j.jnucmat.2019.151893 — (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] Byggmästar J, Granberg FG. LAMMPS Tersoff-ZBL potential for Fe developed by J. Byggmästar and Granberg (2020) v000. OpenKIM; 2020. doi:10.25950/436c99e7 [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. 24 Citations (18 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 . L. Stefanescu, M. Boleininger, and P. Ma, “Athermal swelling and creep of heavily irradiated iron under uniaxial stress,” Physical Review Materials. 2023. link Times cited: 0 Abstract: The athermal irradiation-induced swelling and creep in iron … read more Abstract: The athermal irradiation-induced swelling and creep in iron under the inﬂuence of external uniaxial stress were investigated through atomic scale simulations using the creation-relaxation algorithm. The defect relaxation volume density tensors (or eigenstrains) were evaluated as a function of external uniaxial stress. When the dose exceeded the level at which isolated defects were formed, interstitial-type defect clusters were formed with polarization, resulting in crystal growth in the direction where tensile stress was applied, and growth in the other two perpendicular directions when compressive stress was applied. The concentration of vacancies, isolated self-interstitial atoms, Laves phases clusters, and dislocations in the microstructure were largely unaffected by external stress up to ± 1 GPa. Biased crystal growth was primarily attributed to the anisotropic formation of new lattice planes through the coalescence of interstitial defect clusters, leading to plastic deformation depending on the direction and magnitude of the external stress. read less K. Talaat, B. Cowen, and O. Anderoglu, “Method of information entropy for convergence assessment of molecular dynamics simulations,” Journal of Applied Physics. 2020. link Times cited: 0 Abstract: The lack of a reliable method to evaluate the convergence of… read more Abstract: The lack of a reliable method to evaluate the convergence of molecular dynamics simulations has contributed to discrepancies in different areas of molecular dynamics. In the present work, the method of information entropy is introduced to molecular dynamics for stationarity assessment. The Shannon information entropy formalism is used to monitor the convergence of the atom motion to a steady state in a continuous spatial domain and is also used to assess the stationarity of calculated multidimensional fields such as the temperature field in a discrete spatial domain. It is demonstrated in this work that monitoring the information entropy of the atom position matrix provides a clear indicator of reaching steady state in radiation damage simulations, non-equilibrium molecular dynamics thermal conductivity computations, and simulations of Poiseuille and Couette flow in nanochannels. A main advantage of the present technique is that it is non-local and relies on fundamental quantities available in all molecular dynamics simulations. Unlike monitoring average temperature, the technique is applicable to simulations that conserve total energy such as reverse non-equilibrium molecular dynamics thermal conductivity computations and to simulations where energy dissipates through a boundary as in radiation damage simulations. The method is applied to simulations of iron using the Tersoff/ZBL splined potential, silicon using the Stillinger–Weber potential, and to Lennard–Jones fluid. Its applicability to both solids and fluids shows that the technique has potential for generalization to other areas in molecular dynamics. read less A. Yanilkin, “Simplified atomistic based kinetic model for swelling prediction,” Journal of Nuclear Materials. 2023. link Times cited: 0 S. S. M. N. Souq, F. A. Ghasemi, and M. M. S. Fakhrabadi, “Performance of different traditional and machine learning-based atomistic potential functions in the simulation of mechanical behavior of Fe nanowires,” Computational Materials Science. 2022. link Times cited: 0 L. Zhang, G. Csányi, E. Giessen, and F. Maresca, “Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential,” npj Computational Materials. 2022. link Times cited: 3 J. Gao, E. Gaganidze, and J. Aktaa, “Relative population of 1/2<111> and <100> interstitial loops in alpha-Fe under irradiation: Effects of C15 cluster stability and loop one-dimensional movement,” Acta Materialia. 2022. link Times cited: 5 M. Roldán, F. Sánchez, P. Fernández, C. Ortiz, A. Gómez-Herrero, and D. J. Rey, “Dislocation Loop Generation Differences between Thin Films and Bulk in EFDA Pure Iron under Self-Ion Irradiation at 20 MeV,” Metals. 2021. link Times cited: 3 Abstract: In the present investigation, high-energy self-ion irradiati… read more Abstract: In the present investigation, high-energy self-ion irradiation experiments (20 MeV Fe+4) were performed on two types of pure Fe samples to evaluate the formation of dislocation loops as a function of material volume. The choice of model material, namely EFDA pure Fe, was made to emulate experiments simulated with computational models that study defect evolution. The experimental conditions were an ion fluence of 4.25 and 8.5 × 1015 ions/cm2 and an irradiation temperature of 350 and 450 °C, respectively. First, the ions pass through the samples, which are thin films of less than 100 nm. With this procedure, the formation of the accumulated damage zone, which is the peak where the ions stop, and the injection of interstitials are prevented. As a result, the effect of two free surfaces on defect formation can be studied. In the second type of experiments, the same irradiations were performed on bulk samples to compare the creation of defects in the first 100 nm depth with the microstructure found in the whole thickness of the thin films. Apparent differences were found between the thin foil irradiation and the first 100 nm in bulk specimens in terms of dislocation loops, even with a similar primary knock-on atom (PKA) spectrum. In thin films, the most loops identified in all four experimental conditions were b ±a0<100>{200} type with sizes of hundreds of nm depending on the experimental conditions, similarly to bulk samples where practically no defects were detected. These important results would help validate computational simulations about the evolution of defects in alpha iron thin films irradiated with energetic ions at large doses, which would predict the dislocation nucleation and growth. read less H. Ji et al., “Comparison of interatomic potentials on crack propagation properties in bcc iron,” International Journal of Pressure Vessels and Piping. 2021. link Times cited: 3 Z. Wu, R. Wang, L. Zhu, S. Pattamatta, and D. Srolov, “Revealing and Controlling the Core of Screw Dislocations in BCC Metals.” 2021. link Times cited: 0 Abstract: Body-centred-cubic (BCC) transition metals (TMs) tend to b… read more Abstract: Body-centred-cubic (BCC) transition metals (TMs) tend to be brittle at low temperatures, posing significant challenges in their processing and major concerns for damage tolerance in critical load-carrying applications. The brittleness is largely dictated by the screw dislocation core structure; the nature and control of which has remained a puzzle for nearly a century. Here, we introduce a universal model and a physics-based material index χ that guides the manipulation of dislocation core structure in all pure BCC metals and alloys. We show that the core structure, commonly classified as degenerate (D) or non-degenerate (ND), is governed by the energy difference between BCC and face-centred cubic (FCC) structures and χ robustly captures this key quantity. For BCC TMs alloys, the core structure transition from ND to D occurs when χ drops below a threshold, as seen in atomistic simulations based on nearly all extant interatomic potentials and density functional theory (DFT) calculations of W-Re/Ta alloys. In binary W-TMs alloys, DFT calculations show that χ is related to the valence electron concentration at low to moderate solute concentrations, and can be controlled via alloying. χ can be quantitatively and efficiently predicted via rapid, low-cost DFT calculations for any BCC metal alloys, providing a robust, easily applied tool for the design of ductile and tough BCC alloys. read less L. Liu et al., “Formation mechanism of 〈111〉 interstitial dislocation loops from irradiation-induced C15 clusters in tungsten,” Physical Review Materials. 2021. link Times cited: 8 L. Malerba et al., “Physical mechanisms and parameters for models of microstructure evolution under irradiation in Fe alloys – Part I: Pure Fe,” Nuclear Materials and Energy. 2021. link Times cited: 8 J. Gao, E. Gaganidze, B. Kaiser, and J. Aktaa, “Evolution mechanisms of irradiation-induced helium bubbles, C15 clusters and dislocation loops in ferrite/martensite steels: A cluster dynamics modeling study,” Journal of Nuclear Materials. 2021. link Times cited: 6 R. Collette and J. King, “Molecular dynamics simulations of radiation cascade evolution near cellular dislocation structures in additively manufactured stainless steels,” Journal of Nuclear Materials. 2021. link Times cited: 8 K. Lai, K. Li, H. Wen, Q. Guo, B. Wang, and Y. Zheng, “Synergistic effects of applied strain and cascade overlap on irradiation damage in BCC iron,” Journal of Nuclear Materials. 2020. link Times cited: 9 A. Dubinko, N. Castin, D. Terentyev, G. Bonny, and M. Konstantinović, “Effect of Si–Ni–P on the emergence of dislocations loops in Fe–9Cr matrix under neutron irradiation: TEM study and OKMC modelling,” Journal of Nuclear Materials. 2020. link Times cited: 10 R. Alexander et al., “Interatomic potentials for irradiation-induced defects in iron,” Journal of Nuclear Materials. 2020. link Times cited: 13 S. Y. Korostelev, E. E. Slyadnikov, and I. Turchanovsky, “The resistance of amorphous metals to thermal effects. Molecular dynamics modeling,” PHYSICAL MESOMECHANICS OF CONDENSED MATTER: Physical Principles of Multiscale Structure Formation and the Mechanisms of Nonlinear Behavior: MESO2022. 2023. link Times cited: 0 J. Crocombette and F. Willaime, “Ab Initio Electronic Structure Calculations for Nuclear Materials,” Comprehensive Nuclear Materials. 2020. link Times cited: 11 I. Toda-Caraballo, J. Wróbel, and D. Nguyen-Manh, “Generalized universal equation of states for magnetic materials: A novel formulation for an interatomic potential in Fe,” Physical Review Materials. 2022. link Times cited: 0 Y. Wang et al., “Machine-learning interatomic potential for radiation damage effects in bcc-iron,” Computational Materials Science. 2022. link Times cited: 7 A. Goryaeva et al., “Compact A15 Frank-Kasper nano-phases at the origin of dislocation loops in face-centred cubic metals,” Nature Communications. 2023. link Times cited: 2 A. Esfandiarpour, J. Byggmästar, J. Balbuena, M. Caturla, K. Nordlund, and F. Granberg, “Effect of cascade overlap and C15 clusters on the damage evolution in Fe: An OKMC study,” Materialia. 2022. link Times cited: 2 A. Goryaeva et al., “Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W,” Physical Review Materials. 2021. link Times cited: 20 Abstract: .… read more Abstract: . read less J. Byggmastar, K. Nordlund, and F. Djurabekova, “Gaussian approximation potentials for body-centered-cubic transition metals,” Physical Review Materials. 2020. link Times cited: 22 Abstract: We develop a set of machine-learning interatomic potentials … read more Abstract: We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect, and surface properties. All potentials are augmented with accurate repulsive potentials, making them applicable to radiation damage simulations involving high-energy collisions. We study melting and liquid properties in detail and use the potentials to provide melting curves up to 400 GPa for all five elements. read less
Funding	Not available

Short KIM ID The unique KIM identifier code.	SM_958863895234_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_TersoffZBL_ByggmastarGranberg_2020_Fe__SM_958863895234_000
DOI	10.25950/436c99e7 https://doi.org/10.25950/436c99e7 https://commons.datacite.org/doi.org/10.25950/436c99e7
KIM Item Type	Simulator Model
KIM API Version	2.1
Simulator Name The name of the simulator as defined in kimspec.edn.	LAMMPS
Potential Type	tersoff
Simulator Potential	tersoff/zbl
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 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
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
N/A Unable to perform verification check due to an error.	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: Fe

Cohesive Energy Graph

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

Species: Fe

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: Fe

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: Fe

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: Fe

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: Fe

Cubic Crystal Basic Properties Table

Species: Fe

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 Fe v004	view	1701
Cohesive energy versus lattice constant curve for diamond Fe v004	view	2162
Cohesive energy versus lattice constant curve for fcc Fe v004	view	2497
Cohesive energy versus lattice constant curve for sc Fe v004	view	1789

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 Fe in AFLOW crystal prototype A_cF4_225_a v002	view	127511
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_cI2_229_a v002	view	73841
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_hP2_194_c v002	view	42897
Equilibrium crystal structure and energy for Fe in AFLOW crystal prototype A_tP28_136_f2ij v002	view	59545

GrainBoundaryCubicCrystalSymmetricTiltRelaxedEnergyVsAngle__TD_410381120771_003

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

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

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

Test	Link to Test Results page	Benchmark time Usertime multiplied by the Whetstone Benchmark. This number can be used (approximately) to compare the performance of different models independently of the architecture on which the test was run. Measured in Millions of Whetstone Instructions (MWI)
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in bcc Fe v001	view	17117593
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in bcc Fe v001	view	96130526
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in bcc Fe v001	view	30365804
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in bcc Fe v001	view	189052455
Relaxed energy as a function of tilt angle for a 100 symmetric tilt grain boundary in fcc Fe v001	view	38299813
Relaxed energy as a function of tilt angle for a 110 symmetric tilt grain boundary in fcc Fe v001	view	185198940
Relaxed energy as a function of tilt angle for a 111 symmetric tilt grain boundary in fcc Fe v001	view	385316147
Relaxed energy as a function of tilt angle for a 112 symmetric tilt grain boundary in fcc Fe v001	view	428010776

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 bcc Fe at 293.15 K under a pressure of 0 MPa v002		view	2734078

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 bcc Fe		view	4486065

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 bcc Fe		view	9988829

Errors

EquilibriumCrystalStructure__TD_457028483760_002

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

No Driver

Verification Check	Error Categories	Link to Error page
MemoryLeak__VC_561022993723_004	other	view
PeriodicitySupport__VC_895061507745_004	other	view

Files

CMakeLists.txt
Fe_Byggmastar.tersoff.zbl
LICENSE
kimprovenance.edn
kimspec.edn
smspec.edn

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Sim_LAMMPS_TersoffZBL_ByggmastarGranberg_2020_Fe__SM_958863895234_000

Verification Check Dashboard

Visualizers (in-page)

BCC Lattice Constant

Cohesive Energy Graph

Diamond Lattice Constant

Dislocation Core Energies

FCC Elastic Constants

FCC Lattice Constant

FCC Stacking Fault Energies

FCC Surface Energies

SC Lattice Constant

Cubic Crystal Basic Properties Table

Tests

Errors

Files

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