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 Materials Science and Engineering

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

1774 Citations (Scopus)

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
Publication status	Published - 2018 Jul 26

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the EPSRC (grant numbers EP/M009580/1, EP/K016288/1 and EP/L016354/1), the Royal Society and the Leverhulme Trust. O.I. acknowledges support from DOD-ONR (N00014-16-1-2311) and an Eshelman Institute for Innovation award.

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

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AU - Davies, Daniel W.

AU - Cartwright, Hugh

AU - Isayev, Olexandr

AU - Walsh, Aron

N1 - Funding Information: Acknowledgements This work was supported by the EPSRC (grant numbers EP/M009580/1, EP/K016288/1 and EP/L016354/1), the Royal Society and the Leverhulme Trust. O.I. acknowledges support from DOD-ONR (N00014-16-1-2311) and an Eshelman Institute for Innovation award. Publisher Copyright: © 2018, Macmillan Publishers Ltd., part of Springer Nature.

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N2 - 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

Bibliographical note

All Science Journal Classification (ASJC) codes

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