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 Chemistry

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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.

Original language	English
Pages (from-to)	547-555
Number of pages	9
Journal	Nature
Volume	559
Issue number	7715
Early online date	25 Jul 2018
DOIs	https://doi.org/10.1038/s41586-018-0337-2
Publication status	Published - 26 Jul 2018

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10.1038/s41586-018-0337-2

Walsh_MachineLearning_FinalSubmission
This is the Authors' Accepted Manuscript of an article published in Butler, KT, Davies, DW, Cartwright, H, Isayev, O & Walsh, A 2018, 'Machine learning for molecular and materials science' Nature, vol. 559, no. 7715, pp. 547-555 and available via: http://dx.doi.org/10.1038/s41586-018-0337-2
Accepted author manuscript, 314 KBLicence: Unspecified

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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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Machine learning for molecular and materials science

Abstract

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