Linear Regression Model for Predicting Allyl Alcohol C–O Bond Activity under Palladium Catalysis

C–O bond activation assisted by activators such as Brønsted acids greatly improves the value of allyl alcohol in allylation; thus, understanding and predicting the activation energy barrier is of paramount importance. Herein, we reveal that multiple linear regression (MLR) analysis is a suitable tool for unifying and correlating different activators and ligands of Pd-catalyzed C–O bond activation of allyl alcohols. We obtain a simple model predicting activation energy barriers with different activators and ligands of 393 calculated data points. Statistical tools and extensive molecular featurization have guided the development of an inclusive linear regression model, providing a predictive platform and readily interpretable descriptors. It was found that easily available descriptors, such as the acidity (pKa) of the activators, and the EHOMO, vertical ionization potential (VIP), and bond angle (φP-Pd-P) of the ligands, can well describe the combined influences of steric and electronic effects, including hydrogen-bonding interactions. Overall, this strategy highlights the utility of MLR analysis in exploring mechanistically driven correlations across a diverse chemical space in organometallic chemistry and presents an applicable workflow for C–O bond activation.