Randomly sample new training state {i new } from p Compute inelastic rates using high-accuracy QCT calculations for the new sample, 9: until N = N desired 10: return I, N desired and Rates calculations and, consequently, all the necessary information to the networks for generating a physics-based surrogate that effectively generalizes to unseen states and translational temperatures. Altogether, 90 initial states for each translation temperature are used for running QCT simulations, producing the rate coefficients for training. The inelastic and exchange rates from each of these initial states form the training data set for the DeepONets, while the dissociation rates are used to train the FNN.