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The chemistry of errors

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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Correspondence to Jacqueline M. Cole.

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Cole, J.M. The chemistry of errors. Nat. Chem. 14, 973–975 (2022).

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