3 Methods Table shows the summary of the data sets used in this work. A total of three data sets are prepared: (1) a pre-training set containing the reactions from Grambow et al., (2) a fine-tuning set containing the reactions from Harms et al., and (3) an experimental test set from our prior study. The data sets include diverse range of neutral closed-shell and free radical reactions and nonionic solvents. For both pre-training and fine-tuning sets, ∆∆G ‡ solv and ∆∆H ‡ solv are computed for each reaction-solvent pair with the COSMO-RS calculations based on the geometries obtained from Grambow et al. and Harms et al. The ML model is trained, validated, and tested on the computed data, and the experimental set is used as an additional test set for the final error assessment. The reaction and solvent information is stored as atom-mapped reaction SMILES and solvent SMILES in all data sets. We separated the computed data into the pre-training and fine-tuning sets because the two data sets significantly differ in the types of reactions included and the level of theory used for geometry optimizations. The pre-training set is the largest, but the majority of its reactions are uncommon reactions with high gas phase barrier heights (e.g. E a > 50 kcal/mol), and it does not contain any reactions that are bimolecular in both forward and reverse directions (e.g. only A → B, A + B → AB, and AB → A + B reactions appear). In contrast, the fine-tuning set is smaller but contains more common reactions. To leverage the different types of data, we employ a transfer learning approach in which the model is first pre-trained on the reactions from Grambow et al. and subsequently fine-tuned on the reactions from Harms et al. Details on each data set and the computational method are described below, and all data sets are provided as a part of the Supporting Information.