Dataset
Silica_NPJCM_2022
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| Name | Silica_NPJCM_2022 |
|---|---|
| Extended ID | Silica_NPJCM_2022__Erhard-Rohrer-Albe-Deringer__DS_14m394gnh3ae_0 |
| 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 Ã…-1 were used. |
| Authors |
Linus C. Erhard Jochen Rohrer Karsten Albe Volker L. Deringer |
| DOI |
10.60732/c2bee5fa
https://commons.datacite.org/doi.org/10.60732/c2bee5fa https://doi.datacite.org/dois/10.60732%2Fc2bee5fa https://doi.org/10.60732/c2bee5fa Cite as: Erhard, L. C., Rohrer, J., Albe, K., and Deringer, V. L. "Silica NPJCM 2022." ColabFit, 2022. https://doi.org/10.60732/c2bee5fa. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
O (66.47%)
Si (33.53%) |
| Number of Configurations | 3,074 |
| Number of Atoms | 268,118 |
| Publication Link | https://doi.org/10.1038/s41524-022-00768-w |
| Data Source Link | https://doi.org/10.5281/zenodo.6353683 |
| Configuration Sets by Name | |
| Configuration Sets by ID | |
| ColabFit ID | DS_14m394gnh3ae_0 |
| Downloads | 11 |
| Files | colabfitspec.json |
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