High‐Throughput Kinetics Enable Predicting Reactivity Across Mechanisms of Acid/Base Catalysis in Water

Abstract

In water, acids and bases actively participate in reactions through different concurrently operative mechanisms of catalysis. As each mechanism shows a different dependence on reaction parameters such as pH, catalyst identity, and concentration, quantitative prediction of chemical reactivity becomes a multiparameter problem. Yet, systematic experimental datasets that sample this multidimensional parameter space, and predictive frameworks capable of quantitatively resolving and predicting reactivity across conditions, are still lacking. In this work, we establish a high‐throughput kinetic workflow that enables sampling and analyzing the multiparameter space of reactivity in water. We apply this approach to the acid/base‐catalyzed addition of a model nitrogen nucleophile to a pool of carbonyl compounds, generating an experimental dataset of 6889 rate constants. Using a mechanism‐guided model, we demonstrate that absolute rate constants and the contributions of individual mechanisms can be quantitatively predicted for specific carbonyl compounds. In a complementary approach, supervised machine learning enables the prediction of reactivity across substrates and reaction conditions. This work establishes a quantitative framework for resolving mechanisms and analyzing reactivity in complex aqueous environments, providing a foundation for predictive modeling of reactions in water.