Using Data Science Tools to Explore Rate Matching in a Nickel-Catalyzed Cross-Electrophile Coupling of Alkyl and Aryl Halides (Cl, Br) with a Tridentate Monoanionic Ligand

Abstract Nickel-catalyzed cross-electrophile coupling (XEC) between aryl and alkyl electrophiles has emerged as an enabling synthetic method, yet predicting the outcomes of specific electrophile pairs is difficult due to the complexity of the mechanistic pathways. It has been proposed that “rate matching” between the oxidative addition of the aryl halide and the rate of formation of the alkyl radical, in addition to the stability of the Ni(II)–Ar complex, dictates the yield of the cross-coupling product. To probe this quantitatively, we developed a new tridentate monoanionic ligand that activatesalkyl and aryl chlorides and exhibits well-behaved electrochemistry, allowing the measurement of relative rate constants. This provides a framework to interrogate the rate-matching concept using both aryl and alkyl bromides and chlorides in cross-electrophile coupling. By combining an electroanalytical workflow with machine learning, the prediction of activation rate constants for all aryl/alkyl chlorides and bromides was achieved and correlated with reaction yields. We found that productive coupling occurs in a specific rate regime, and the synthetic performance could be predictably modulated by choosing the “matched” halide (either chloride or bromide) for each coupling partner.