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Machine learning made easy for optimizing chemical reactions

An accessible machine-learning tool has been developed that can accelerate the optimization of a wide range of synthetic reactions — and reveals how cognitive bias might have undermined optimization by humans.
  1. Jason E. Hein

    1. Jason E. Hein is in the Department of Chemistry, University of British Columbia, Vancouver V6T 1Z1, Canada.

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The optimization of reactions used to synthesize target compounds is pivotal to chemical research and discovery, whether in developing a route for manufacturing a life-saving medicine1 or unlocking the potential of a new material2. But reaction optimization requires iterative experiments to balance the often conflicting effects of numerous coupled variables, and frequently involves finding the sweet spot among thousands of possible sets of experimental conditions. Expert synthetic chemists currently navigate this expansive experimental void using simplified model reactions, heuristic approaches and intuition derived from observation of experimental data3. Writing in Nature, Shields et al.4 report machine-learning software that can optimize diverse classes of reaction with fewer iterations, on average, than are needed by humans.

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Nature 590, 40-41 (2021)

doi: https://doi.org/10.1038/d41586-021-00209-6

References

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