Journal
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
Volume -, Issue -, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/anie.202313638
Keywords
Cross-Dimerization; High-Throughput Experimentation; Machine Learning; Sulfoxonium Ylides; Unsymmetrical Alkenes
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In this study, a novel approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies is presented to achieve selective cross-dimerization of sulfoxonium ylides via iridium catalysis. The method provides a convenient and efficient route for the synthesis of a variety of valuable unsymmetrical E-alkenes with high stereoselectivities.
A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide-sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed.
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