Dataset
AFF_JCP_2022
Download Dataset XYZ file
| Name | AFF_JCP_2022 |
|---|---|
| Extended ID | AFF_JCP_2022_LiZhouSebastianWuGu__DS_294zg1j9lo6j_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 |
| Elements |
C (33.33%) H (36.89%) N (14.0%) O (15.78%) |
| Number of Data Objects | 143,767 |
| Number of Configurations | 143,769 |
| Number of Atoms | 1,911,219 |
| Links |
https://doi.org/10.1063/5.0088017 https://github.com/UncertaintyQuantification/AFF/tree/master |
| Configuration Sets by Name |
AFF_JCP_2022-aspirin — Aspirin configurations from AFF_JCP_2022 dataset AFF_JCP_2022-uracil — Uracil configurations from AFF_JCP_2022 dataset AFF_JCP_2022-alkane — Alkane configurations from AFF_JCP_2022 dataset AFF_JCP_2022-alpha-glucose — alpha-glucose configurations from AFF_JCP_2022 dataset |
| Configuration Sets by ID |
CS_sf84359olcdr_0 CS_kk3ioqwim4j4_0 CS_me2zvzn1iep5_0 CS_7lms9qkotnor_0 |
| Data Objects | Too many to display |
| ColabFit ID | DS_294zg1j9lo6j_0 |
| Files | colabfitspec.json |
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