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Machine learning for molecular and materials science

Nature volume 559, pages 547–555 (2018)Cite this article

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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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Fig. 1: Evolution of the research workflow in computational chemistry.
Fig. 2: Errors that arise in machine-learning approaches.
Fig. 3: The generative adversarial network (GAN) approach to molecular discovery.

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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.

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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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Authors and 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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  1. Keith T. Butler
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All authors contributed equally to the design, writing and editing of the manuscript.

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Butler, K.T., Davies, D.W., Cartwright, H. et al. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). https://doi.org/10.1038/s41586-018-0337-2

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