Active learning high coverage sets of complementary reaction conditions

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.