It is also essential to note that the effect of solvent friction has been neglected. Our ML model predicts the solvent effects on the activation barriers, but diffusion control should be taken into account if the predicted reaction rate is above the diffusion limit for a bimolecular reaction. Moreover, the majority of the reactions in the training set are unimolecular in a either forward or reverse direction (e.g. A → B, A + B → AB, and AB → A + B), and the bimolecular H-abstraction in the fine-tuning set is the only reaction family that are bimolecular in both forward and reverse directions. We assume the learning of unimolecular systems can also provide good approximations for bimolecular systems. However, the model could have larger predictions errors for a new class of bimolecular reactions, and caution should be made for such reactions.