Dataset

water_ice_JCP_2020


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Name water_ice_JCP_2020
Extended ID water_ice_JCP_2020_SchranBrezinaMarsalek__DS_p2aaxa2vfnr6_0
Description Starting from a single reference ab initio simulation, we use active learning to expand into new state points and to describe the quantum nature of the nuclei. The final model, trained on 814 reference calculations, yields excellent results under a range of conditions, from liquid water at ambient and elevated temperatures and pressures to different phases of ice, and the air-water interface — all including nuclear quantum effects.
Authors Christoph Schran
Kyrstof Brezina
Ondrej Marsalek
DOI 10.60732/d1024453
https://commons.datacite.org/doi.org/10.60732/d1024453
https://doi.datacite.org/dois/10.60732%2Fd1024453
https://doi.org/10.60732/d1024453

Cite as: Schran, C., Brezina, K., and Marsalek, O. "water ice JCP 2020." ColabFit, 2023. https://doi.org/10.60732/d1024453.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements H (66.67%)
O (33.33%)
Number of Data Objects 8,814
Number of Configurations 8,000
Number of Atoms 2,088,000
Links https://doi.org/10.5281/zenodo.4004590
https://doi.org/10.1063/5.0016004
Configuration Sets by Name water_ice_jcp_2020_test_set — Configurations from the test set for the C-NNP model from water_ice_JCP_2020
Configuration Sets by ID CS_lh5t24gqu1h9_0
Data Objects
ColabFit ID DS_p2aaxa2vfnr6_0
Files colabfitspec.json

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