Machine learning in chemical reaction space

Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reactio...

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Autores principales:	Sina Stocker, Gábor Csányi, Karsten Reuter, Johannes T. Margraf
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/61fa2a4df63746c2accee2753ea074c2
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id	oai:doaj.org-article:61fa2a4df63746c2accee2753ea074c2
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spelling	oai:doaj.org-article:61fa2a4df63746c2accee2753ea074c22021-12-02T15:39:16ZMachine learning in chemical reaction space10.1038/s41467-020-19267-x2041-1723https://doaj.org/article/61fa2a4df63746c2accee2753ea074c22020-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19267-xhttps://doaj.org/toc/2041-1723Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.Sina StockerGábor CsányiKarsten ReuterJohannes T. MargrafNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Sina Stocker Gábor Csányi Karsten Reuter Johannes T. Margraf Machine learning in chemical reaction space
description	Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.
format	article
author	Sina Stocker Gábor Csányi Karsten Reuter Johannes T. Margraf
author_facet	Sina Stocker Gábor Csányi Karsten Reuter Johannes T. Margraf
author_sort	Sina Stocker
title	Machine learning in chemical reaction space
title_short	Machine learning in chemical reaction space
title_full	Machine learning in chemical reaction space
title_fullStr	Machine learning in chemical reaction space
title_full_unstemmed	Machine learning in chemical reaction space
title_sort	machine learning in chemical reaction space
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/61fa2a4df63746c2accee2753ea074c2
work_keys_str_mv	AT sinastocker machinelearninginchemicalreactionspace AT gaborcsanyi machinelearninginchemicalreactionspace AT karstenreuter machinelearninginchemicalreactionspace AT johannestmargraf machinelearninginchemicalreactionspace
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Machine learning in chemical reaction space

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