Reinforcement learning prioritizes general applicability in reaction optimization

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired. While many approaches exist to evaluate the general applicability of developed conditions, a universal approach to efficiently discover such conditions during optimizations de novo is rare. In this work, we report the design, implementation, and application of reinforcement learning bandit optimization models to identify generally applicable conditions in a variety of chemical transformations. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions. A palladium-catalyzed imidazole C–H arylation reaction and an aniline amide coupling reaction were investigated experimentally to demonstrate utilities of our learning model in practice.