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MACHINE LEARNING

The chemistry of errors

Nature Chemistry volume 14, pages 973–975 (2022)Cite this article

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The application of machine learning to big data, to make quantitative predictions about reaction outcomes, has been fraught with failure. This is because so many chemical-reaction data are not fit for purpose, but predictions would be less error-prone if synthetic chemists changed their reaction design and reporting practices.

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Fig. 1: Aspects of data that influence the efficacy of data-driven materials discovery.

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Authors and Affiliations

  1. Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Jacqueline M. Cole

  2. ISIS Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, UK

    Jacqueline M. Cole

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  1. Jacqueline M. Cole
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Correspondence to Jacqueline M. Cole.

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The author declares no competing interests.

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Cole, J.M. The chemistry of errors. Nat. Chem. 14, 973–975 (2022). https://doi.org/10.1038/s41557-022-01028-6

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