In this study, we present a ML model that can predict kinetic solvent effects for a wide range of neutral reactions and solvents only based on atom-mapped reaction SMILES and solvent SMILES strings. More precisely, the model predicts the solvation free energy and solvation enthalpy of activation (∆∆G ‡ solv , ∆∆H ‡ solv ) for a reaction-solvent pair, which can be used to estimate a relative rate constant between a solution phase and a gas phase reaction or between the reaction in different solvents. Our model adopts a CGR GCNN architecture with separate GCNN layers for solvent molecular encoding. A large, diverse set of training data containing over 28,000 reactions and 295 solvents is generated in this work by performing ab initio COSMO-RS 32 calculations. The performance of the model on unseen reactions is rigorously assessed by comparing the ML predictions with both COSMO-RS calculations and experimental data. A transfer learning approach and various additional features are explored to further improve the model. Our ML model can provide accurate predictions of relative rate constants, and together with the existing predictive models or databases for gas phase rate constants (e.g. RMG database ), it can provide the estimates of absolute rate constants for many different liquid phase reactions.