where b represents the bias of a final linear transformation. The branch subnetwork comprises four input features (i) logarithm of vibrational energy, (ii) logarithm of rotational energy, (iii) logarithm of the outer turning point, and (iv) translational temperature. We choose the same four quantities as inputs for the trunk. However, while the inputs to the branch are the property values corresponding to initial levels, the trunk inputs are selected to be the differences between the values of final and initial states. The only exception is the translational temperature, for which the same value (in Kelvin) is given as input to both the branch and the trunk. As previously mentioned, the motivation for choosing vibrational and rotational properties as inputs is drawn from the paper by Sahai et al. . At the same time, we choose to use energies rather than quantum numbers to ensure generalizability, as the spacing of the energy levels is non-uniform. Together with these two quantities, we also try multiple combinations of different input variables from the following set of diatomic properties: outer turning point, inner turning point, energies and locations at the centrifugal barrier, and vibrational period. We find that only the outer turning point, r o , is necessary for accurately training the surrogate components. Once again, we emphasize that two separate DeepONets are employed for the energy transfer rates: one for the inelastic and one for the excitation rate coefficients. Schematics of these networks are reported in Figs. and, together with the corresponding number of layers, number of neurons, and activation functions. Drop-out and L1 and L2 regularization schemes are used during training to improve the generalization capabilities. The drop-out rate and L1 and L2 regularization parameters are (10 -2 , 10 -5 , 10 -5 ) for the inelastic rate coefficients and (10 -3 , 10 -4 , 10 -4 ) for the exchange reaction rate coefficients. The loss function, L , used for optimizing the neural network parameters is the mean absolute percentage error, which is defined as: