Machine learning-guided yield optimization for palladaelectro-catalyzed annulation reaction

Electrosynthesis has become an increasingly popular platform in modern organic chemistry, possessing distinctive features and reaction parameters like applied current/potential, electrodes, electrolyte systems, and cell design. While these unique features give chemists more opportunities to control reactivity and selectivity, they also increase the dimensionalities of a reaction and complicate the interactions between variables, making the optimization more challenging. Herein, we present a machine learning (ML) workflow that leverages physical organic descriptor-based yield prediction and orthogonal experimental design to strike a delicate balance between the need for sampling diversity and the pursuit of yield improvements, thereby efficiently identifying ideal conditions for enantioselective palladaelectro-catalyzed annulation from extensive synthetic space. This work shows the potential of synergizing organic electrochemistry and a data-driven approach to tackle multidimensional chemical optimization problems.