Machine Learning-Driven Exploration of Suzuki–Miyaura Cross-Coupling with a Polymer-Supported Pd in Continuous-Flow System

In this study, we developed a hybrid methodology for machine learning-driven optimization of continuous-flow reaction with a polymer-supported Pd catalyst, aiming to boost both productivity and elucidate influencing factors in the reaction conditions. A porous polymer bearing phosphine ligand was prepared by polymerization-induced phase separation, and Pd was coordinated to the support to construct the flow reactor. Suzuki–Miyaura cross-coupling reactions were performed in the continuous-flow system. Combining Bayesian optimization and linear regression realized optimization of continuous-flow conditions and analysis of key influencing factors, demonstrating the utility of the present machine learning method. Indeed, the continuous-flow system with the monolith reactor was also applicable to a range of chloroarenes, which emphasized the importance of our catalytic system for fine chemical production.