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Machine learning for molecular and materials science

Naturevolume 559pages547555 (2018) | Download Citation

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.

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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.

Reviewer information

Nature thanks F.-X. Coudert, M. Waller and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Affiliations

  1. ISIS Facility, Rutherford Appleton Laboratory, Harwell Campus, Harwell, UK

    • Keith T. Butler
  2. Department of Chemistry, University of Bath, Bath, UK

    • Daniel W. Davies
  3. Department of Chemistry, Oxford University, Oxford, UK

    • Hugh Cartwright
  4. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    • Olexandr Isayev
  5. Department of Materials Science and Engineering, Yonsei University, Seoul, South Korea

    • Aron Walsh
  6. Department of Materials, Imperial College London, London, UK

    • Aron Walsh

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All authors contributed equally to the design, writing and editing of the manuscript.

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The authors declare no competing interests.

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Correspondence to Olexandr Isayev or Aron Walsh.

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