DUNN_WenTadmor_2019v2_C__MO_956135237832_000
| Title
A single sentence description.
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Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v000 |
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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. |
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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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A dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems with a dropout ratio of 0.2. This is an ensemble model consisting of 100 different network structures obtained by dropout. Before dropout, there are three hidden layers each containing 128 neurons; each neuron in the hidden layers has probability 0.2 of being removed from the network. By default, the model will run in the 'mean' mode where the output energy, forces, and virial are obtained by averaging over the 100 ensembles. If desired, one can set the 'active_member_id' to '0' to use the fully-connected structure or to '1, 2, ..., 100' to use a single ensemble member. When multiple ensemble members are used, the ensemble average of the energy and forces are what are ultimately returned for a given configuration. |
| Species
The supported atomic species.
| C |
| Disclaimer
A statement of applicability provided by the contributor, informing users of the intended use of this KIM Item.
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None |
| Contributor |
Mingjian Wen |
| Maintainer |
Mingjian Wen |
| Published on KIM | 2019 |
| How to Cite |
This Model originally published in [1] is archived in OpenKIM [2-5]. [1] Wen M, Tadmor EB. Uncertainty quantification in molecular simulations with dropout neural network potentials. npj Computational Materials. 2020;6(1). doi:10.1038/s41524-020-00390-8 — (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] Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v000. OpenKIM; 2019. doi:10.25950/5cdb2c9f [3] A dropout uncertainty neural network (DUNN) model driver v000. OpenKIM; 2019. doi:10.25950/9573ca43 [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_956135237832_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.
| DUNN_WenTadmor_2019v2_C__MO_956135237832_000 |
| DOI |
10.25950/5cdb2c9f https://doi.org/10.25950/5cdb2c9f https://search.datacite.org/works/10.25950/5cdb2c9f |
| 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 DUNN__MD_292677547454_000 |
| Driver | DUNN__MD_292677547454_000 |
| KIM API Version | 2.0 |
| Potential Type | dunn |
| Grade | Name | Category | Brief Description | Full Results | Aux File(s) |
|---|---|---|---|---|---|
| P | vc-periodicity-support | mandatory | Periodic boundary conditions are handled correctly; see full description. |
Results | Files |
| P | 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 |
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.
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.
(No matching species)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.
(No matching species)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.
(No matching species)Cubic Crystal Basic Properties Table
Species: CCreators:
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 | 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) |
|---|---|---|---|
| Cohesive energy versus lattice constant curve for bcc C v004 | view | 17982 |
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 | 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 bcc C at zero temperature v006 | view | 43124 |
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 | 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 zero-temperature lattice constant for bcc C v007 | view | 40626 | |
| Equilibrium zero-temperature lattice constant for diamond C v007 | view | 730404 |
| DUNN_WenTadmor_2019v2_C__MO_956135237832_000.txz | Tar+XZ | Linux and OS X archive |
| DUNN_WenTadmor_2019v2_C__MO_956135237832_000.zip | Zip | Windows archive |
This Model requires a Model Driver. Archives for the Model Driver DUNN__MD_292677547454_000 appear below.
| DUNN__MD_292677547454_000.txz | Tar+XZ | Linux and OS X archive |
| DUNN__MD_292677547454_000.zip | Zip | Windows archive |