Machine learning coarse grained models for water

A computationally efficient description of ice-water systems at the mesoscopic scale is challenging due to system size and timescale limitations. Here the authors develop a machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models.

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Autores principales:	Henry Chan, Mathew J. Cherukara, Badri Narayanan, Troy D. Loeffler, Chris Benmore, Stephen K. Gray, Subramanian K. R. S. Sankaranarayanan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e694cefa440e4058adee19ae8691e135
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spelling	oai:doaj.org-article:e694cefa440e4058adee19ae8691e1352021-12-02T17:33:11ZMachine learning coarse grained models for water10.1038/s41467-018-08222-62041-1723https://doaj.org/article/e694cefa440e4058adee19ae8691e1352019-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-08222-6https://doaj.org/toc/2041-1723A computationally efficient description of ice-water systems at the mesoscopic scale is challenging due to system size and timescale limitations. Here the authors develop a machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models.Henry ChanMathew J. CherukaraBadri NarayananTroy D. LoefflerChris BenmoreStephen K. GraySubramanian K. R. S. SankaranarayananNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-14 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Henry Chan Mathew J. Cherukara Badri Narayanan Troy D. Loeffler Chris Benmore Stephen K. Gray Subramanian K. R. S. Sankaranarayanan Machine learning coarse grained models for water
description	A computationally efficient description of ice-water systems at the mesoscopic scale is challenging due to system size and timescale limitations. Here the authors develop a machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models.
format	article
author	Henry Chan Mathew J. Cherukara Badri Narayanan Troy D. Loeffler Chris Benmore Stephen K. Gray Subramanian K. R. S. Sankaranarayanan
author_facet	Henry Chan Mathew J. Cherukara Badri Narayanan Troy D. Loeffler Chris Benmore Stephen K. Gray Subramanian K. R. S. Sankaranarayanan
author_sort	Henry Chan
title	Machine learning coarse grained models for water
title_short	Machine learning coarse grained models for water
title_full	Machine learning coarse grained models for water
title_fullStr	Machine learning coarse grained models for water
title_full_unstemmed	Machine learning coarse grained models for water
title_sort	machine learning coarse grained models for water
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/e694cefa440e4058adee19ae8691e135
work_keys_str_mv	AT henrychan machinelearningcoarsegrainedmodelsforwater AT mathewjcherukara machinelearningcoarsegrainedmodelsforwater AT badrinarayanan machinelearningcoarsegrainedmodelsforwater AT troydloeffler machinelearningcoarsegrainedmodelsforwater AT chrisbenmore machinelearningcoarsegrainedmodelsforwater AT stephenkgray machinelearningcoarsegrainedmodelsforwater AT subramaniankrssankaranarayanan machinelearningcoarsegrainedmodelsforwater
_version_	1718380034011430912

Machine learning coarse grained models for water

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