Dataset

Si-H-GAP_training


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Species content of dataset


Name :
Si-H-GAP_training
Authors :
Davis Unruh, Reza Vatan Meidanshahi, Stephen M. Goodnick, Gábor Csányi, Gergely T. Zimányi
Description :
A set of training configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. Includes virial sigmas used for configurations used in the corresponding publication (virial-sigma-paper) as well as an alternate configuration defined by doubled virial sigma prefactors (from 0.025 to 0.05).
DOI :
https://doi.org/10.60732/43a0cef7 https://commons.datacite.org/doi.org/10.60732/43a0cef7
Cite As :
Unruh, D., Meidanshahi, R. V., Goodnick, S. M., Csányi, G., and Zimányi, G. T. "Si-H-GAP training." ColabFit, 2023. https://doi.org/10.60732/43a0cef7.
ColabFit ID :
DS_8arvjtldu4fw_0
Extended ID :
Si-H-GAP_training__Unruh-Meidanshahi-Goodnick-Csanyi-Zimanyi__DS_8arvjtldu4fw_0
Date Added :
2023-11-22
License :
CC-BY-4.0
Downloads :
37
Num. Configurations :
392
Num. Atoms :
65,909
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
H (7.8%) Si (92.2%)
Methods :
DFT-PBE
Software :
Quantum ESPRESSO
Publication Link :
https://doi.org/10.1103/PhysRevMaterials.6.065603
Data Source Link :
https://github.com/dgunruh/Si-H-GAP
Spec File :
colabfitspec.json
Configuration Sets by Name :
Configuration Sets by ID :
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Si-H-GAP_reference
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