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

441 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

All Science Journal Classification (ASJC) codes

  • General

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    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