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

ABSTRACT: Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp³-sp² cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical prope

SUBMITTER: Li X 

PROVIDER: S-EPMC11321994 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature