Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds

Abstract

Data-driven methodology plays an important role in the rapid identification of appropriate chemical conditions, however, optimization of multiple variables in the flow reaction remains challenging. Here, the authors report a Bayesian optimization-assisted multi-parameter screening to predict the suitable conditions to achieve the efficient synthesis of biaryl compounds in a flow system.