Dataset

AFF_JCP_2022


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Species content of dataset


Name :
AFF_JCP_2022
Authors :
Hao Li, Musen Zhou, Jessalyn Sebastian, Jianzhong Wu, Mengyang Gu
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.
DOI :
https://doi.org/10.60732/82344f5c https://commons.datacite.org/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.
ColabFit ID :
DS_5lhmgnxhuia3_0
Extended ID :
AFF_JCP_2022__Li-Zhou-Sebastian-Wu-Gu__DS_5lhmgnxhuia3_0
Date Added :
2023-06-30
License :
GPL-3.0
Downloads :
57
Num. Configurations :
143,756
Num. Atoms :
1,911,045
Calculated Property Types :
atomic_forces energy
Elements :
C (33.33%) H (36.89%) N (14.0%) O (15.78%)
Methods :
DFT-PBE-vdW-TS
Software :
Q-Chem
Publication Link :
https://doi.org/10.1063/5.0088017
Data Source Link :
https://github.com/UncertaintyQuantification/AFF/tree/master
Spec File :
colabfitspec.json
Configuration Sets by Name :
Configuration Sets by ID :
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