Combining High‐Throughput Experimentation and Automated Flow for Bayesian Optimization of a Metallaphotoredox Reaction

Abstract

ABSTRACT In this work, a metallaphotoredox C(sp 2 )–C(sp 3 ) coupling between aryl (pseudo)halides and alkyl boronic acid pinacol esters was optimized using a two‐stage workflow. In the first stage, high‐throughput experimentation (HTE) is used in conjunction with Bayesian optimization (BO) to select promising discrete parameter values for five categorical reaction variables, including photocatalyst, nickel precursor, nickel ligand, solvent, and amine, from a search space of 30,000 conditions within 120 experiments per substrate across four aryl (pseudo)halides, representing <0.5% of the space. Crucially, the approach proved able to autonomously identify reactivity trends reported in literature without any prior knowledge. In the second stage, the optimal conditions for each substrate are transferred to flow, where eight continuous parameters, including residence time, reaction stoichiometry, temperature, and light intensity, are fine‐tuned. The sequential strategy delivers DMF‐ and precious‐metal‐free protocols for bromides and chlorides and, for the first time, extended this coupling reaction to aryl triflates.