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A dataset of fully quantum flux-flux correlation functions and reaction rateconstants was constructed for organic heterogeneous catalytic surfacereactions. Gaussian process regressors were successfully fitted to trainingdata to predict previously unseen test set reaction rate constant products andCauchy fits of the flux-flux correlation function. The optimal regressorprediction mean absolute percent errors were on the order of 0.5% for test setreaction rate constant products and 1.0% for test set flux-flux correlationfunctions. The Gaussian process regressors were accurate both when looking atkinetics at new temperatures and reactivity of previously unseen reactions andprovide a significant speedup respect to the computationally demanding timepropagation of the flux-flux correlation function.

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