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MACHINE LEARNING

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.

References

  1. Baptista de Castro, P. et al. NPG Asia Mater. 12, 35 (2020).

    Article  Google Scholar 

  2. Gómez-Bombarelli, R. et al. Nat. Mater. 15, 1120–1127 (2016).

    Article  Google Scholar 

  3. Strieth-Kalthoff, F. et al. Angew. Chem. Int. Ed. 61, e202204647 (2022).

    Article  CAS  Google Scholar 

  4. Perera, D. et al. Science 359, 429–434 (2018).

    Article  CAS  Google Scholar 

  5. Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Science 360, 186–190 (2018).

    Article  CAS  Google Scholar 

  6. Li, Z. et al. Chem. Mater. 32, 5650–5663 (2020).

    Article  CAS  Google Scholar 

  7. Burger, B. et al. Nature 583, 237–241 (2020).

    Article  CAS  Google Scholar 

  8. MacLeod, B. P. et al. Science Adv. 6, eaaz8867 (2020).

    Article  CAS  Google Scholar 

Download references

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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). https://doi.org/10.1038/s41557-022-01028-6

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