Current potential: SW_LeeHwang_2012GGA_Si__MO_040570764911_001
Does the citing paper use the current potential to generate results displayed in the paper?
Provide us with identifying information so that we know you are not a bot (you will not be added to a mailing list):
Title
A single sentence description.
Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); GGA parameterization v001
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 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 .
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.
A force-matching method is employed to optimize the parameters of the Stillinger–Weber (SW) interatomic potential for calculation of the lattice thermal conductivity of silicon. The parameter fitting is based on first-principles density functional calculations of the restoring forces for atomic displacements. The thermal conductivities of bulk crystalline Si at 300–500 K estimated using nonequilibrium molecular dynamics with the modified parameter set show excellent agreement with existing experimental data. The force-matching-based parameterization is shown to provide improved estimation of thermal conductivity, as compared to the original SW parameter set, through analysis of phonon density of states and phonon dispersion relations. Two parameterizations are provided in the paper. one fit to DFT/LDA and the other to DFT/GGA. This model is the GGA parameterization.
Species
The supported atomic species.
Si
Disclaimer
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
This Model originally published in [1] is archived in OpenKIM [2-5].
[1] Lee Y, Hwang GS. Force-matching-based parameterization of the Stillinger-Weber potential for thermal conduction in silicon. Phys Rev B. 2012;85(12):125204. doi:10.1103/PhysRevB.85.125204 — (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] Tadmor EB, Lee Y, Hwang GS. Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); GGA parameterization v001. OpenKIM; 2021. doi:10.25950/d84bb56f
[3] Wen M, Afshar Y, Stillinger FH, Weber TA. Stillinger-Weber (SW) Model Driver v005. OpenKIM; 2021. doi:10.25950/934dca3e
[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
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.
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).
The letter grade A was assigned because the normalized error in the computation was 1.45560e-10 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.
The model is C^3 continuous. This means that the model has continuous energy and continuous derivatives at least up to order 3. (Derivatives beyond this order were not tested.)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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)