Dataset
AFF_JCP_2022
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| Name | AFF_JCP_2022 |
|---|---|
| Extended ID | AFF_JCP_2022__Li-Zhou-Sebastian-Wu-Gu__DS_5lhmgnxhuia3_0 |
| Description | Approximately 145,000 configurations of alkane, aspirin, alpha-glucose and uracil, partly taken from the MD-17 dataset, used in training an 'Atomic Neural Net' model. |
| Authors |
Hao Li Musen Zhou Jessalyn Sebastian Jianzhong Wu Mengyang Gu |
| DOI |
10.60732/82344f5c
https://commons.datacite.org/doi.org/10.60732/82344f5c https://doi.datacite.org/dois/10.60732%2F82344f5c https://doi.org/10.60732/82344f5c Cite as: Li, H., Zhou, M., Sebastian, J., Wu, J., and Gu, M. "AFF JCP 2022." ColabFit, 2023. https://doi.org/10.60732/82344f5c. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
C (33.33%)
H (36.89%) N (14.0%) O (15.78%) |
| Number of Configurations | 143,756 |
| Number of Atoms | 1,911,045 |
| Publication Link | https://doi.org/10.1063/5.0088017 |
| Data Source Link | https://github.com/UncertaintyQuantification/AFF/tree/master |
| Configuration Sets by Name | |
| Configuration Sets by ID | |
| ColabFit ID | DS_5lhmgnxhuia3_0 |
| Downloads | 14 |
| Files | colabfitspec.json |
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