MEAM_LAMMPS_AslamBaskesDickel_2019_FeMnSiC__MO_427873955970_002
| Title
A single sentence description.
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MEAM Potential for the Fe-Mn-Si-C system developed by Aslam et al. (2019) v002 |
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| Citations
This panel presents the list of papers that cite the interatomic potential whose page you are on (by its primary sources given below in "How to Cite"). Articles marked by the green star have been determined to have used the potential in computations (as opposed to only citing it as background information) by a machine learning (ML) algorithm developed by the KIM Team that analyzes the full text of the papers. Articles that do not use it are marked with a null symbol, and in cases where no information is available a question mark is shown. The full text of the articles used to train the ML algorithm is provided by the Allen Institute for AI through the Semantic Scholar project. The word cloud to the right is built from the abstracts of the primary sources and using papers to give a sense of the types of physical phenomena to which this interatomic potential is applied. IMPORTANT NOTE: Usage can only be determined for articles for which Semantic Scholar can provide OpenKIM with the full text. Where this is not the case, we ask the community for help in determining usage. If you know whether an article did or did not use a potential, let us know by clicking the cloud icon by the article and completing a one question form. |
The word cloud indicates applications of this Potential. The bar chart shows citations per year of this Potential. 
Help us to determine which of the papers that cite this potential actually used it to perform calculations. If you know, click the .
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| 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.
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The MEAM potential for the Fe, Mn, Si, and C system is developed by employing a hierarchical multiscale modeling paradigm to simulate low-alloy steels. Experimental information alongside first-principles calculations based on Density Functional Theory served as calibration data to develop and upscale the potential. The cohesive energy, lattice parameters, elastic constants, and vacancy and interstitial formation energies are used as target data for calibrating the single element potentials. The heat of formation and elastic constants of binary compounds and substitutional and interstitial formation energies serve as binary potential calibration data, while substitutional and interstitial pair binding energies aid in developing the ternary potential. Molecular dynamics simulations employing this model predict the thermal expansion coefficient, heat capacity, self-diffusion coefficients, and stacking fault energy for steel alloys comparable to those reported in the literature. NOTE: The parameters submitted with the journal article as supplementary material have a small error. The paramter files included with the KIM model are correct. |
| Species
The supported atomic species.
| C, Fe, Mn, Si |
| Disclaimer
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
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None |
| Content Origin | NIST IPRP (https://www.ctcms.nist.gov/potentials/entry/2019--Aslam-I-Baskes-M-I-Dickel-D-E-et-al--Fe-Mn-Si-C#FeMnSiC) |
| Contributor |
Yaser Afshar |
| Maintainer |
Yaser Afshar |
| Developer |
Imran Aslam Michael I. Baskes Doyl E. Dickel Sara Adibi Bin Li Hongjoo Rhee Mohsen Asle Zaeem Mark F. Horstemeyer |
| Published on KIM | 2023 |
| How to Cite |
This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Aslam I, Baskes MI, Dickel DE, Adibi S, Li B, Rhee H, 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;8:100473. doi:10.1016/j.mtla.2019.100473 — (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] Aslam I, Baskes MI, Dickel DE, Adibi S, Li B, Rhee H, et al. MEAM Potential for the Fe-Mn-Si-C system developed by Aslam et al. (2019) v002. OpenKIM; 2023. doi:10.25950/5bec2aa2 [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. |
| Funding | Not available |
| Short KIM ID
The unique KIM identifier code.
| MO_427873955970_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_AslamBaskesDickel_2019_FeMnSiC__MO_427873955970_002 |
| DOI |
10.25950/5bec2aa2 https://doi.org/10.25950/5bec2aa2 https://search.datacite.org/works/10.25950/5bec2aa2 |
| 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_AslamBaskesDickel_2019_FeMnSiC__MO_427873955970_001 |
| Grade | Name | Category | Brief Description | Full Results | Aux File(s) |
|---|---|---|---|---|---|
| P | vc-species-supported-as-stated | mandatory | The model supports all species it claims to support; see full description. |
Results | Files |
| P | vc-periodicity-support | mandatory | Periodic boundary conditions are handled correctly; see full description. |
Results | Files |
| P | vc-permutation-symmetry | mandatory | Total energy and forces are unchanged when swapping atoms of the same species; see full description. |
Results | Files |
| B | vc-forces-numerical-derivative | consistency | Forces computed by the model agree with numerical derivatives of the energy; see full description. |
Results | Files |
| F | 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 | vc-objectivity | informational | Total energy is unchanged and forces transform correctly under rigid-body translation and rotation; see full description. |
Results | Files |
| P | vc-inversion-symmetry | informational | Total energy is unchanged and forces change sign when inverting a configuration through the origin; see full description. |
Results | Files |
| N/A | 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 | 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 | 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 |
BCC Lattice Constant
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.
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.
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.
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.
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.
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.
Cubic Crystal Basic Properties Table
Species: CSpecies: Fe
Species: Mn
Species: Si
Creators: Daniel S. Karls
Contributor: karls
Publication Year: 2019
DOI: https://doi.org/10.25950/b47dd4c4
Given an xyz file corresponding to a finite cluster of atoms, this Test Driver computes the total potential energy and atomic forces on the configuration. The positions are then relaxed using conjugate gradient minimization and the final positions and forces are recorded. These results are primarily of interest for training machine-learning algorithms.