The fine-tuning set is randomly split into a 80 % training and a 20 % validation set using one fold. We did not construct a separate test set from the fine-tuning set. Instead, the experimental set is used as a final reaction-split test set for the fine-tuned model. We ensured that none of the experimental reactions (both forward and reverse) appear in the pre-training and fine-tuning sets. The schematic of the ML architecture is depicted in Figure . Our model takes the atommapped reaction SMILES and solvent SMILES as inputs and predicts ∆∆G ‡ solv and ∆∆H ‡ solv at 298 K. The model is constructed using Chemprop, an open-source software that uses a directed message passing neural network (D-MPNN) for chemical and reaction property predictions. A D-MPNN is a type of GCNN that converts atom and bond features into a latent representation of a molecule through bond-level message passing. To encode a reaction, we adopt the established CGR representation as it has shown to outperform other representations for various reaction property predictions. The CGR is a superposition of the reactant and product graphs, which mimics the 2D-structure of the TS. In our model, the CGR representation is constructed from the atom-mapped reactants and products and passed into a D-MPNN to give a reaction embedding. A separate D-MPNN is employed to convert a solvent graph into a solvent molecular embedding. The learned reaction and solvent representations are then concatenated together and passed into a feed forward neural network (FNN) to predict the regression targets. The initial atom and bond features are generated using RDKit within Chemprop and include several features such as atom type, bond type, formal charge, chirality, and etc. The hyperparameters are optimized via 47 iterations of Bayesian optimization with the Hyperopt package. Only the training/validation set of the pre-training data is used for the hyperparameter optimization to prevent data leakage. The full list of atom and bond features and the optimized hyperparameters can be found in Supporting Information Tables and.