Machine learning for molecular and materials science

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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Correspondence to Olexandr Isayev or Aron Walsh.

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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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