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

Abstract

Traditional optimization methods using one variable at a time approach waste time and chemicals and assume that different parameters are independent from one another. Hence, a simpler, more practical, and rapid process for predicting reaction conditions that can be applied to several manufacturing environmentally sustainable processes is highly desirable. In this study, biaryl compounds were synthesized efficiently using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening, which employs one-hot encoding and appropriate acquisition function, rapidly predicted the suitable conditions for the synthesis of 2-amino-2′-hydroxy-biaryls (maximum yield of 96%). The established protocol was also applied in an optimization process for the efficient synthesis of 2,2′-dihydroxy biaryls (up to 97% yield). The optimized reaction conditions were successfully applied to gram-scale synthesis. We believe our algorithm can be beneficial as it can screen a reactor design without complicated quantification and descriptors.

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.