Active learning high coverage sets of complementary reaction conditions†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2025-02-17 DOI:10.1039/D4DD00365A
Sofia L. Sivilotti, David M. Friday and Nicholas E. Jackson
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Abstract

Chemical reaction conditions capable of producing high yields over diverse reactants are a desired component of nearly all chemical and materials discovery campaigns. While much work has been done to discover individual general reaction conditions, any single conditions are necessarily limited over increasingly diverse chemical spaces. A potential solution to this problem is to identify small sets of complementary reaction conditions that, when combined, cover a larger chemical space than any one general reaction condition. In this work, we analyze experimentally derived datasets to assess the relative performance of individual general reaction conditions vs. sets of complementary reaction conditions. We then propose and benchmark active learning methods to efficiently discover these complimentary sets of conditions. The results show the value of active learning in identifying complementary sets of reaction conditions and provide an avenue for improving synthetic hit rates in high-throughput synthesis campaigns.

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Back cover Unveiling CO2 reactivity with data-driven methods† Dissecting errors in machine learning for retrosynthesis: a granular metric framework and a transformer-based model for more informative predictions SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions† Active learning high coverage sets of complementary reaction conditions†
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