Chemistry

Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery

X. Li, Y. Che, et al.

Discover a groundbreaking two-step data-driven approach for synthesizing organic photoredox catalysts (OPCs) and optimizing reactions for metallophotocatalysis, as demonstrated by Xiaobo Li, Yu Che, and their team. They efficiently explored only 2.4% of the catalyst space to identify highly competitive OPC formulations.

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Abstract

This paper presents a two-step data-driven approach for the targeted synthesis of organic photoredox catalysts (OPCs) and subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp³-sp² cross-coupling of amino acids with aryl halides. A Bayesian optimization (BO) strategy, coupled with molecular descriptors, identified promising OPCs from a virtual library of 560 candidates. This led to OPC formulations competitive with iridium catalysts, exploring only 2.4% of the available catalyst formulation space.

Publisher

Nature Chemistry

Published On

Jun 11, 2024

Authors

Xiaobo Li, Yu Che, Linjiang Chen, Tao Liu, Kewei Wang, Lunjie Liu, Haofan Yang, Edward O. Pyzer-Knapp, Andrew I. Cooper

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