Chemistry

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

M. Kondo, H. D. P. Wathsala, et al.

Discover how Masaru Kondo and colleagues revolutionized the synthesis of biaryl compounds using a flow system and a Brønsted acid catalyst. Their innovative approach, enhanced by Bayesian optimization, achieved impressive yields and streamlined the process, paving the way for efficient gram-scale production.

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Abstract

Traditional optimization methods using a one-variable-at-a-time approach waste time and chemicals and assume that different parameters are independent. This study efficiently synthesized biaryl compounds using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening rapidly predicted suitable conditions for synthesizing 2-amino-2'-hydroxy-biaryls (96% maximum yield) and 2,2'-dihydroxy biaryls (up to 97% yield). The optimized conditions were successfully applied to gram-scale synthesis. The algorithm efficiently screens reactor design without complex quantification and descriptors.

Publisher

Communications Chemistry

Published On

Nov 10, 2022

Authors

Masaru Kondo, H. D. P. Wathsala, Mohamed S. H. Salem, Kazunori Ishikawa, Satoshi Hara, Takayuki Takaai, Takashi Washio, Hiroaki Sasai, Shinobu Takizawa

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