Photocatalytic Cross‐Coupling of Phenols and Heteroaryl Halides With Machine Learning‐Guided Reaction Prediction

Abstract

ABSTRACT Developing sustainable methods for C(sp 2 )─C(sp 2 ) bond formation that avoid transition‐metals and prefunctionalized substrates remains a central goal in synthetic chemistry. Phenols and N ‐heteroarenes (azines) are abundantly available, yet their cross‐coupling is hindered by mismatched redox properties and chemoselectivity issues. Herein, we report a photochemical strategy that couples phenols with heteroaryl halides under redox‐neutral conditions using an organic dye photocatalyst and base. Concurrent oxidation of the phenol component and reduction of the azine component generates complementary radicals that cross‐couple efficiently, delivering moderate to high yields (up to 91%) with high functional group tolerance. Mechanistic experiments and density functional theory (DFT) studies elucidate the radical reaction pathways, while substrate clustering, high‐throughput experimentation (HTE), and machine learning (ML) enable prediction of C–C versus S N Ar reactivity across broad chemical space.