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

213 Citations (Scopus)
101 Downloads (Pure)

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
Early online date25 Jul 2018
DOIs
Publication statusPublished - 26 Jul 2018

ASJC Scopus subject areas

  • General

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