Machine learning for molecular and materials science

Keith T. Butler; Daniel W. Davies; Hugh Cartwright; Olexandr Isayev; Aron Walsh

doi:10.1038/s41586-018-0337-2

Machine learning for molecular and materials science

Keith T. Butler
, Daniel W. Davies
, Hugh Cartwright
, Olexandr Isayev
, Aron Walsh

Department of Physics

Research output: Contribution to journal › Review article › peer-review

3749 Scopus citations

Abstract

Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

Original language	English
Pages (from-to)	547-555
Number of pages	9
Journal	Nature
Volume	559
Issue number	7715
DOIs	https://doi.org/10.1038/s41586-018-0337-2
State	Published - 26 Jul 2018

Bibliographical note

Publisher Copyright:
© 2018, Macmillan Publishers Ltd., part of Springer Nature.

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abstract = "Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.",

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AB - Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

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Machine learning for molecular and materials science

Abstract

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