Download Dataset XYZ file

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
DOI 10.60732/82344f5c

Cite as: Li, H., Zhou, M., Sebastian, J., Wu, J., and Gu, M. "AFF JCP 2022." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements C (33.33%)
H (36.89%)
N (14.0%)
O (15.78%)
Number of Data Objects 143,770
Number of Configurations 143,770
Number of Atoms 1,911,240
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
Data Objects Too many to display
ColabFit ID DS_5lhmgnxhuia3_0
Files colabfitspec.json

No uploaded content is transferred in ownership from the original creators to ColabFit. All content is distributed under the license specified by its contributor who has stated that he or she has the authority to share it under the specified license.