Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence.

The fate of a catalyst or catalytic intermediate, i.e., its speciation, in situ is a critical aspect of the efficiency of a catalyst as well as the overall reactivity and selectivity of the catalyzed transformation. However, the precise factors that dictate catalyst speciation are rarely understood and trial-and-error approaches frequently prevail. To address this challenge and develop predictive tools to guide ligand selection for a desired metal speciation in a catalytically relevant context, we evaluated the feasibility of machine learning combined with computational activation barrier predictions to achieve CO2 insertion at room temperature for the sterically least hindered (and most vulnerable) Ni(I)-Ph complexes, which constitute key catalytic intermediates. Following an in depth computational rationalization on the origin of reactivity difference of Ni(I) versus Ni(II) toward CO2 insertion, we subsequently pursued machine learning to identify ligands that favor the critical Ni(I) oxidation state. To this end, a descriptor database was constructed in silico. Subsequent application of machine learning led to the prediction of numerous ligands that favor the more reactive Ni(I)-Ph intermediate and oxidation state, which were subsequently filtered for candidates that also show desired room temperature reactivity through the calculation of activation barriers. Ultimately, a set of representative candidates was synthesized and experimentally tested for CO2 insertion, confirming their reactivity and alignment with computational predictions. This work offers a blueprint for creating and analyzing virtual databases to predict ligands-including never synthesized ones-that control metal complex oxidation state, nuclearity, and reactivity.