Machine learning the quantum flux–flux correlation function for catalytic surface reactions

Abstract

A dataset of fully quantum flux–flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to predict previously unseen test set reaction rate constant products and Cauchy fits of the flux–flux correlation function. The optimal regressor prediction mean absolute percent errors were on the order of 1.0% for both test set reaction rate constant products and test set flux–flux correlation functions. The Gaussian process regressors were accurate both when looking at kinetics at new temperatures and reactivity of previously unseen reactions and provide a significant speedup respect to the computationally demanding time propagation of the flux–flux correlation function.