  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 0.5% for test set
reaction rate constant products and 1.0% for 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.
