Table : Test errors of different models and additional (addit.) features. The best combination of the model and additional feature is marked in bold. For the pre-training set, the errors on the reaction split are available for all models while the errors on the solvent split are only available for the pre-trained model and shown in parentheses. The standard deviations are calculated between the 5 folds for the pre-trained set. All test errors are reported in kcal/mol. in all cases as the COSMO-RS method, which was used to generate the training data, has higher calculation errors for ∆∆H ‡ solv . Furthermore, ∆∆H ‡ solv generally has a larger magnitude than ∆∆G ‡ solv , which leads to larger absolute errors. The results also show that the pre-trained model has much lower errors on the solvent split than the reaction split. The model is able to provide very accurate predictions on unseen solvents with 295 training solvents whereas it has much higher errors on unseen reactions even with 26,448 training reactions. We believe this is because the chemical space of viable solvents is not as nearly big as that of reactions. Furthermore, the reaction split is a more challenging task since the model has to infer the TS information from the reactants and products. The fine-tuned model was not separately tested on the solvent split as it was trained on all solvents that are found in the fine-tuning set. Since the major limitation is on the reaction split, we expect the fine-tuned model to have a similarly low error on unseen solvents.