Revealing Kinetic Features of a Macrocyclization Reaction Using Machine-Learning-Augmented Data

We developed a method to reveal the kinetic features of macrocyclization using machine-learning-augmented data. Thirty-six experimental yield datasets were expanded to two hundred datasets through machine learning, enabling kinetic analyses of concentration-dependent yields. This augmented data allowed the chemistry underlying the predictive black box to be elucidated, providing quantitative insights such as rate constants and effective molarity through least-squares fitting of differential rate equations. These quantitative measures offered physicochemical explanations for the optimal conditions identified by the machine learning. Additionally, linear relationships between effective molarity and strain energy were found, highlighting key aspects of oligomeric macrocyclization via nickel-mediated coupling.