Dataset

Si-H-GAP_training

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Name :

Si-H-GAP_training

Extended ID :

Si-H-GAP_training__Unruh-Meidanshahi-Goodnick-Csanyi-Zimanyi__DS_8arvjtldu4fw_0

ColabFit ID :

DS_8arvjtldu4fw_0

Files :

colabfitspec.json

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).

Authors :

Davis Unruh, Reza Vatan Meidanshahi, Stephen M. Goodnick, Gábor Csányi, Gergely T. Zimányi

DOI :

10.60732/43a0cef7 https://commons.datacite.org/doi.org/10.60732/43a0cef7 https://doi.datacite.org/dois/10.60732%2F43a0cef7 https://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.
For other citation formats, see the DataCite Fabrica page for this dataset.

Num. Configurations :

392

Num. Atoms :

65,909

Downloads :

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

Configuration Sets by Name :

Configuration Sets by ID :

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