Dataset
CA-9_training
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| Name | CA-9_training |
|---|---|
| Extended ID | CA-9_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_9cdtww9pcjqd_0 |
| Description | Configurations from CA-9 dataset used for training NNP_CA-9 potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials |
| Authors |
Daniel Hedman Tom Rothe Gustav Johansson Fredrik Sandin J. Andreas Larsson Yoshiyuki Miyamoto |
| DOI |
10.60732/8b765383
https://commons.datacite.org/doi.org/10.60732/8b765383 https://doi.datacite.org/dois/10.60732%2F8b765383 https://doi.org/10.60732/8b765383 Cite as: Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. "CA-9 training." ColabFit, 2023. https://doi.org/10.60732/8b765383. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
C (100.0%)
|
| Number of Configurations | 39,993 |
| Number of Atoms | 2,195,024 |
| Publication Link | https://doi.org/10.1016/j.cartre.2021.100027 |
| Data Source Link | https://doi.org/10.24435/materialscloud:6h-yj |
| Configuration Sets by Name | |
| Configuration Sets by ID | |
| ColabFit ID | DS_9cdtww9pcjqd_0 |
| Downloads | 16 |
| Files | colabfitspec.json |
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