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Machine learning for molecular and materials science

Author

Listed:
  • Keith T. Butler

    (Rutherford Appleton Laboratory, Harwell Campus)

  • Daniel W. Davies

    (University of Bath)

  • Hugh Cartwright

    (Oxford University)

  • Olexandr Isayev

    (University of North Carolina at Chapel Hill)

  • Aron Walsh

    (Yonsei University
    Imperial College London)

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.

Suggested Citation

  • Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
    • Handle: RePEc:nat:nature:v:559:y:2018:i:7715:d:10.1038_s41586-018-0337-2
    DOI: 10.1038/s41586-018-0337-2
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