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

Nature. 2018 Jul;559(7715):547-555. doi: 10.1038/s41586-018-0337-2. Epub 2018 Jul 25.

Authors

Keith T Butler¹, Daniel W Davies², Hugh Cartwright³, Olexandr Isayev⁴, Aron Walsh^{5

6}

Affiliations

¹ ISIS Facility, Rutherford Appleton Laboratory, Harwell Campus, Harwell, UK.
² Department of Chemistry, University of Bath, Bath, UK.
³ Department of Chemistry, Oxford University, Oxford, UK.
⁴ Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. olexandr@olexandrisayev.com.
⁵ Department of Materials Science and Engineering, Yonsei University, Seoul, South Korea. a.walsh@imperial.ac.uk.
⁶ Department of Materials, Imperial College London, London, UK. a.walsh@imperial.ac.uk.

PMID: 30046072
DOI: 10.1038/s41586-018-0337-2

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.

Publication types

Review
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.