Dataset

Silica_NPJCM_2022


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Species content of dataset


Name :
Silica_NPJCM_2022
Authors :
Linus C. Erhard, Jochen Rohrer, Karsten Albe, Volker L. Deringer
Description :
This dataset was created for the purpose of training an MLIP for silica (SiO2). For initial DFT computations, GPAW (in combination with ASE) was used with LDA, PBE and PBEsol functionals; and VASP with the SCAN functional. All calculations used the projector augmented-wave method. After comparison, it was found that SCAN performed best, and all values were recalculated using SCAN. An energy cut-off of 900 eV and a k-spacing of 0.23 Å-1 were used.
DOI :
https://doi.org/10.60732/c2bee5fa https://commons.datacite.org/doi.org/10.60732/c2bee5fa
Cite As :
Erhard, L. C., Rohrer, J., Albe, K., and Deringer, V. L. "Silica NPJCM 2022." ColabFit, 2023. https://doi.org/10.60732/c2bee5fa.
ColabFit ID :
DS_14m394gnh3ae_0
Extended ID :
Silica_NPJCM_2022__Erhard-Rohrer-Albe-Deringer__DS_14m394gnh3ae_0
Date Added :
2023-10-03
License :
CC-BY-4.0
Downloads :
45
Num. Configurations :
3,074
Num. Atoms :
268,118
Calculated Property Types :
atomic_forces cauchy_stress energy
Elements :
O (66.47%) Si (33.53%)
Methods :
DFT-SCAN
Software :
VASP
Publication Link :
https://doi.org/10.1038/s41524-022-00768-w
Data Source Link :
https://doi.org/10.5281/zenodo.6353683
Spec File :
colabfitspec.json
Configuration Sets by Name :
Configuration Sets by ID :
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