Retrospective on a decade of machine learning for chemical discovery

Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electron...

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Main Authors:	O. Anatole von Lilienfeld, Kieron Burke
Format:	article
Language:	EN
Published:	Nature Portfolio 2020
Subjects:	Science Q
Online Access:	https://doaj.org/article/df8a833a0e9a45df941e56a6116b39e1
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id	oai:doaj.org-article:df8a833a0e9a45df941e56a6116b39e1
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spelling	oai:doaj.org-article:df8a833a0e9a45df941e56a6116b39e12021-12-02T17:18:08ZRetrospective on a decade of machine learning for chemical discovery10.1038/s41467-020-18556-92041-1723https://doaj.org/article/df8a833a0e9a45df941e56a6116b39e12020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18556-9https://doaj.org/toc/2041-1723Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.O. Anatole von LilienfeldKieron BurkeNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-4 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q O. Anatole von Lilienfeld Kieron Burke Retrospective on a decade of machine learning for chemical discovery
description	Standfirst Over the last decade, we have witnessed the emergence of ever more machine learning applications in all aspects of the chemical sciences. Here, we highlight specific achievements of machine learning models in the field of computational chemistry by considering selected studies of electronic structure, interatomic potentials, and chemical compound space in chronological order.
format	article
author	O. Anatole von Lilienfeld Kieron Burke
author_facet	O. Anatole von Lilienfeld Kieron Burke
author_sort	O. Anatole von Lilienfeld
title	Retrospective on a decade of machine learning for chemical discovery
title_short	Retrospective on a decade of machine learning for chemical discovery
title_full	Retrospective on a decade of machine learning for chemical discovery
title_fullStr	Retrospective on a decade of machine learning for chemical discovery
title_full_unstemmed	Retrospective on a decade of machine learning for chemical discovery
title_sort	retrospective on a decade of machine learning for chemical discovery
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/df8a833a0e9a45df941e56a6116b39e1
work_keys_str_mv	AT oanatolevonlilienfeld retrospectiveonadecadeofmachinelearningforchemicaldiscovery AT kieronburke retrospectiveonadecadeofmachinelearningforchemicaldiscovery
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Retrospective on a decade of machine learning for chemical discovery

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