Accurate prediction of reaction rates is essential for modeling a variety of chemical kinetic systems such as pyrolysis, 1,2 polymerization, oxidative degradation, and atmospheric chemistry. Detailed kinetic models enable one to predict key products, identify major kinetic pathways, and optimize reaction conditions for complex chemical systems. Kinetic mechanisms often involve hundreds to tens of thousands of elementary reactions, and a fast, high-throughput method to estimate reaction rates is thus needed. Ab initio methods like quantum mechanics/molecular mechanics (QM/MM) can provide accurate predictions of rate constants, but their high computational cost has been a major limiting factor for large-scale, automated predictions. As more kinetic data become available, data-driven approaches such as linear group contribution, decision tree based rate rules, and machine learning (ML) models have emerged as more popular choices for estimating kinetic parameters. Several ML models have successfully predicted barrier heights and rate constants of diverse gas phase reactions only based on readily available 2D information (e.g. SMILES strings) of reactants and products. However, such data-driven models for liquid/solution phase reactions have been lightly investigated with limited applicability, and most approaches rely on the ab initio methods with either implicit or explicit solvation models. Solvents can have significant impacts on reaction rates and outcomes, and it is crucial to accurately predict these kinetic solvent effects. Recent research efforts have been devoted to employing ML (e.g. deep neural network) for free energy predictions of condensed phase reactions. Many of these studies combine the ML models with semiempirical or lower-level QM/MM methods to obtain the energy predictions that match the accuracy of higher-level QM/MM methods. For example, Gómez-Flores et al. used a ML approach to predict the energy difference between the density functional tight-binding model and other higher level QM methods for a thiol-disulfide exchange reaction in water. In a study by Pan et al., a ML model was trained to reproduce ab initio QM/MM potentials in free energy simulations for the aqueous Menshutkin reaction between ammonia and chloromethane. Farrar and Grayson 28 employed ML models to predict DFT-quality activation barriers for various nitro-Michael addition reactions in toluene based on the features generated from semi-empirical methods. These approaches, however, require semi-empirical QM/MM steps that are less suitable for instantaneous, automatic rate predictions. Furthermore, their models are limited to a single solvent and need the 3D coordinates or QM features of reactants and transition states as inputs, which are not readily available.