{"title":"Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well","author":[{"last_name":"Monserrat","first_name":"Bartomeu","full_name":"Monserrat, Bartomeu"},{"last_name":"Brandenburg","first_name":"Jan Gerit","full_name":"Brandenburg, Jan Gerit"},{"full_name":"Engel, Edgar A.","first_name":"Edgar A.","last_name":"Engel"},{"last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632"}],"doi":"10.48550/arXiv.2006.13316","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.13316"}],"_id":"9699","oa_version":"Submitted Version","abstract":[{"text":"We investigate the structural similarities between liquid water and 53 ices, including 20 known crystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the local environments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of the\r\nices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.","lang":"eng"}],"article_number":"2006.13316","publication":"arXiv","publication_status":"submitted","type":"preprint","extern":"1","date_updated":"2023-05-10T10:17:48Z","language":[{"iso":"eng"}],"day":"23","external_id":{"arxiv":["2006.13316"]},"year":"2020","status":"public","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2020-06-23T00:00:00Z","article_processing_charge":"No","date_created":"2021-07-20T11:25:15Z","month":"06","citation":{"short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, ArXiv (n.d.).","mla":"Monserrat, Bartomeu, et al. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” <i>ArXiv</i>, 2006.13316, doi:<a href=\"https://doi.org/10.48550/arXiv.2006.13316\">10.48550/arXiv.2006.13316</a>.","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2006.13316\">10.48550/arXiv.2006.13316</a>","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well,” <i>arXiv</i>. .","ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. arXiv, 2006.13316.","chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2006.13316\">https://doi.org/10.48550/arXiv.2006.13316</a>.","apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., &#38; Cheng, B. (n.d.). Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2006.13316\">https://doi.org/10.48550/arXiv.2006.13316</a>"}}