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 143,770
Number of Atoms 1,911,240
Links https://github.com/UncertaintyQuantification/AFF/tree/master
https://doi.org/10.1063/5.0088017
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_0qqpkmn13elf_0
CS_prz0q62dqk8n_0
CS_ljltnmxl5kbx_0
CS_0khowajhl1dt_0
Calculated Properties
ColabFit ID DS_5lhmgnxhuia3_0
Files colabfitspec.json

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