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
Name: water_ice_JCP_2020
Extended ID: water_ice_JCP_2020__Schran-Brezina-Marsalek__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
Calculated Property Types:
atomic_forces
cauchy_stress
energy
Elements:
H (66.67%)
O (33.33%)
Methods:
DFT-revPBE0+D3
Software:
CP2K
Number of Configurations: 8,814
Number of Atoms: 2,304,144
Publication Link: https://doi.org/10.1063/5.0016004
Data Source Link: https://doi.org/10.5281/zenodo.4004590
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