Machine learning for molecular and materials science

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

Research output: Contribution to journalReview article

215 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 languageEnglish
Pages (from-to)547-555
Number of pages9
JournalNature
Volume559
Issue number7715
DOIs
Publication statusPublished - 2018 Jul 26

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Materials science
Learning systems
Artificial intelligence
Molecules
Direction compound

All Science Journal Classification (ASJC) codes

  • General

Cite this

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547-555. https://doi.org/10.1038/s41586-018-0337-2
Butler, Keith T. ; Davies, Daniel W. ; Cartwright, Hugh ; Isayev, Olexandr ; Walsh, Aron. / Machine learning for molecular and materials science. In: Nature. 2018 ; Vol. 559, No. 7715. pp. 547-555.
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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. https://doi.org/10.1038/s41586-018-0337-2

Machine learning for molecular and materials science. / Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh; Isayev, Olexandr; Walsh, Aron.

In: Nature, Vol. 559, No. 7715, 26.07.2018, p. 547-555.

Research output: Contribution to journalReview article

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Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2018 Jul 26;559(7715):547-555. https://doi.org/10.1038/s41586-018-0337-2