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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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 0.1. 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 0.1 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 |
Developer |
Mingjian Wen Ellad B. Tadmor |
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] Wen M, Tadmor EB. Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v000. OpenKIM; 2019. doi:10.25950/44b7f4ed [3] Wen M, Tadmor EB. 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. |
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.
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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Funding | Not available |
Short KIM ID
The unique KIM identifier code.
| MO_584345505904_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_2019v1_C__MO_584345505904_000 |
DOI |
10.25950/44b7f4ed https://doi.org/10.25950/44b7f4ed https://commons.datacite.org/doi.org/10.25950/44b7f4ed |
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) |
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P | vc-periodicity-support | mandatory | Periodic boundary conditions are handled correctly; see full description. |
Results | Files |
P | 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-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 |
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.
(No matching species)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.
(No matching species)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)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.
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) |
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Cohesive energy versus lattice constant curve for bcc C v004 | view | 128887 | |
Cohesive energy versus lattice constant curve for diamond C v004 | view | 1342640 | |
Cohesive energy versus lattice constant curve for fcc C v004 | view | 201586 | |
Cohesive energy versus lattice constant curve for sc C v004 | view | 17903 |
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) |
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Elastic constants for bcc C at zero temperature v006 | view | 201008 | |
Elastic constants for fcc C at zero temperature v006 | view | 180149 | |
Elastic constants for sc C at zero temperature v006 | view | 48775 |
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 | 58313 | |
Equilibrium zero-temperature lattice constant for diamond C v007 | view | 680784 | |
Equilibrium zero-temperature lattice constant for fcc C v007 | view | 75682 | |
Equilibrium zero-temperature lattice constant for sc C v007 | view | 39092 |
Test | Error Categories | Link to Error page |
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Elastic constants for diamond C at zero temperature v001 | other | view |
Test | Error Categories | Link to Error page |
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Equilibrium crystal structure and energy for C in AFLOW crystal prototype A_hR4_166_2c v000 | other | view |
Equilibrium crystal structure and energy for C in AFLOW crystal prototype A_oI120_71_lmn6o v000 | other | view |
DUNN_WenTadmor_2019v1_C__MO_584345505904_000.txz | Tar+XZ | Linux and OS X archive |
DUNN_WenTadmor_2019v1_C__MO_584345505904_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 |