High‐Throughput Experimentation and Machine Learning‐Assisted Optimization of Iridium‐Catalyzed Cross‐Dimerization of Sulfoxonium Ylides

Abstract

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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“…High-throughput experimentation (HTE) is a methodological approach that leverages robotics, automation, and artificial intelligence (AI) to conduct a large number of chemical experiments simultaneously and rapidly. [106][107][108][109][110][111] This approach is useful for research processes, enabling scientists to explore vast chemical spaces and reaction conditions in a fraction of the time required by https://doi.org/10.26434/chemrxiv-2024-cdm8w ORCID: https://orcid.org/0000-0002-6447-557X Content not peer-reviewed by ChemRxiv. License: CC BY-NC 4.0 traditional methods.…”