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

Autor: Li X; Key Laboratory of the Ministry of Education for Advanced Catalysis Materials, Zhejiang Key Laboratory for Reactive Chemistry on Solid Surfaces, Zhejiang Normal University, Jinhua, China. xiaobo.li@zjnu.edu.cn.; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK. xiaobo.li@zjnu.edu.cn., Che Y; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK., Chen L; School of Chemistry and School of Computer Science, University of Birmingham, Birmingham, UK. l.j.chen@bham.ac.uk., Liu T; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK., Wang K; Department of Chemistry and Chemical Engineering, Shanxi Datong University, Datong, China., Liu L; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK., Yang H; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK.; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK., Pyzer-Knapp EO; IBM Research, Daresbury, UK. epyzerk3@uk.ibm.com., Cooper AI; Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool, UK. aicooper@liverpool.ac.uk.; Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Liverpool, UK. aicooper@liverpool.ac.uk.
Jazyk: angličtina
Zdroj: Nature chemistry [Nat Chem] 2024 Aug; Vol. 16 (8), pp. 1286-1294. Date of Electronic Publication: 2024 Jun 11.
DOI: 10.1038/s41557-024-01546-5
Abstrakt: 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 sp 3 -sp 2 cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions).
(© 2024. The Author(s).)
Databáze: MEDLINE
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