Machine learning for molecular and materials science

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

Research output: Contribution to journalReview articlepeer-review

1774 Citations (Scopus)


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
Issue number7715
Publication statusPublished - 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.

All Science Journal Classification (ASJC) codes

  • General


Dive into the research topics of 'Machine learning for molecular and materials science'. Together they form a unique fingerprint.

Cite this