Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes

Abstract

Reaching optimal reaction conditions is crucial to achieve high yields, minimal by-products, and environmentally sustainable chemical reactions. With the recent rise of artificial intelligence, there has been a shift from traditional Edisonian trial-and-error optimization to more data-driven and automated approaches, which offer significant advantages. Here, we showcase the capabilities of this integrated platform, we conducted simultaneous optimizations for the iodoalkylation of four different terminal alkynes and two reaction routes, using an automation platform combined with a Bayesian optimization framework. Remarkably, we achieved a conversion rate of over 80% for all four substrates in under 22 experiments, covering ca. 0.2% of the combinatorial space. Further analysis allowed us to rationalize the influence of different reaction parameters on the reaction outcomes, demonstrating the potential for expedited reaction condition optimization and the prospect of more efficient chemical processes in the future.