Figure shows the results of the additional features tested with the 500k data set. In all cases, only the RP-solv feature improves the errors and other features do not have noticeable impacts on the model performance. The reactions tend to accelerate/decelerate in a polar solvent when the products are more/less strongly solvated than the reactants. The RPsolv feature, which consists of solvation energy and solvation enthalpy of reactants and products, describe how strongly the reactants and products are solvated in a solvent and therefore helps the ∆∆G ‡ solv and ∆∆H ‡ solv predictions for our model. The results also show that the QM-desc feature worsens the model performance, which is consistent with results from Spiekermann et al. that show the descriptors offer no improvement. The QM-desc feature was predicted by the ML model from Guan et al. which was trained on three classes of substitution reactions. Our data set includes more diverse types of reactions, and therefore, it is likely that their model is not suitable for our reaction data. Moreover, it is possible that the chosen QM descriptors are not related to our targets and act as noise to the model.