We describe the development of machine learning model that predict the solvation energies of a diverse class of molecules in five solvents. The following sections describe how we accomplished this goal by first establishing a database of solvation energies based on an unbiased sampling of molecular structures and then gradually increasing the complexity of machine learning techniques used to learn correlations from this data. development of many machine learning models. Second, the database is diverse. QM9 is based on an exhaustive sampling of molecules, 5,33 which allows us to train and validate our molecules with minimal concern over whether it is learning patterns related to the sampling of data and not the physics of the problem. We used the SMD implicit solvation model in Gaussian to compute the solvation energy for 130 258 molecules from QM9. We selected SMD for its comparable accuracy to explicit solvent models and reasonable correlation with experimental solvation energies at a reduced computational cost. For each molecule, we computed the solvation energy in five solvents that sample a broad range of dielectric constants: acetone ( = 20.49), ethanol ( = 24.85), acetonitrile (ACN) ( = 35.69), dimethyl sulfoxide (DMSO) ( = 46.83), and water ( = 78.36). We selected these solvents to not only to capture differences among the solvation behavior of different molecules, but also selecting solvents of technological relevance (e.g., ACN and water are common solvents for flow batteries ).