Dataset

AFF_JCP_2022



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Name AFF_JCP_2022
Extended ID AFF_JCP_2022_LiZhouSebastianWuGu__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
Elements C (33.33%)
H (36.89%)
N (14.0%)
O (15.78%)
Number of Data Objects 143,767
Number of Configurations 143,770
Number of Atoms 1,911,240
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_0qqpkmn13elf_0
CS_prz0q62dqk8n_0
CS_ljltnmxl5kbx_0
CS_0khowajhl1dt_0
Data Objects Too many to display
ColabFit ID DS_5lhmgnxhuia3_0
Files colabfitspec.json

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