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The promise and pitfalls of AI for molecular and materials synthesis

Nature Computational Science volume 3, pages 362–364 (2023)Cite this article

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As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

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Fig. 1: Opportunities in AI for synthesis science in the style of Bloom’s taxonomy.

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Acknowledgements

N.D. and W.S. acknowledge support through the Dreyfus Program for Machine Learning in the Chemical Sciences and Engineering. C.W.C. thanks the National Science Foundation under grant no. CHE-2144153 and the AI2050 program at Schmidt Futures (grant G-22-64475) for financial support. We thank P.F. Poudeu, J. Neilson, and A. Miura for stimulating conversations.

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Authors and Affiliations

  1. Department of Materials Science & Engineering, University of Michigan, Ann Arbor, MI, USA

    Nicholas David & Wenhao Sun

  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

  3. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Connor W. Coley

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  1. Nicholas David
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  2. Wenhao Sun
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  3. Connor W. Coley
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N.D., W.S. and C.W.C. jointly prepared the manuscript.

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Correspondence to Wenhao Sun or Connor W. Coley.

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The authors declare no competing interests.

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David, N., Sun, W. & Coley, C.W. The promise and pitfalls of AI for molecular and materials synthesis. Nat Comput Sci 3, 362–364 (2023). https://doi.org/10.1038/s43588-023-00446-x

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