Name: water ice JCP 2020
License: CC0-1.0

Dataset

water_ice_JCP_2020

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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 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.
Calculated Property Types	atomic_forces cauchy_stress energy
Elements	H (66.67%) O (33.33%)
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
Calculated Properties	PO_1037822462600865714446724 PO_1049638530599358085443353 PO_1069075293568220754647229 PO_1089450196780017788397492 PO_1095525644027774796443580 PO_1176958036620732549801676 PO_1199327479438346582185113 PO_1199450702104464787347627 PO_1305191386342758940519761 PO_1317353945951890201408578 PO_1324438904892687990865553 PO_2546047216819185781268999 PO_2772234897703988831396285 PO_2968312104595717254245980 PO_2985455504704590109540738 PO_3070836680423697146371866 PO_3163619006470726519277477 PO_3365766710489673670632747 PO_4028497627171503842882278 PO_4373280521336507111332277 PO_4406324459315042095766413 PO_4660414343496550073690392 PO_5306935891470138070223483 PO_5839493458660604080720509 PO_5977224267342379301075707 PO_6419323931731255602682534 PO_6529782644969544596197289 PO_7446975476607243234939877 PO_7723465698609104689319716 PO_7774120347675848950507614 PO_8110198747791524504664673 PO_8354057201018152661062425 PO_8379126951393291039230258 PO_8399421975871640951971115 PO_8627300201225664461194275 PO_8707305757226622386664789 PO_8946989428686287750096351 PO_9425551436787034218570401 PO_9455204447482625323189664 PO_9635473033676221534992247 PO_9733654810296089241351830 PO_9764053749163826870629364
ColabFit ID	DS_p2aaxa2vfnr6_0
Files	colabfitspec.json

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