Here, we report on a new large dataset, QM9-Solvation, of computed solvation energies (∆G solv ) for 130 258 molecules in multiple solvents, and provide an initial set of machine learning models trained using this data. The dataset was generated from DFT simulations using the SMD Solvation Model and is openly available for use by the chemistry community. We also demonstrate a state-of-the-art ML prediction model of molecular solvation energies trained on this database. Our model uses a message-passing architecture that includes properties of both the solvent and solute, which we demonstrate is capable of learning solvent/solute relationships from over a half-million training entries. We established a domain of applicability region for this model and find that it is possible to predict solvation energies of molecules larger than those in our training set, and have made the models freely available through GitHub and DLHub. We envision that our database and molecules will help spur rapid advancements in the ability for scientists to efficiently assess the solvation properties of molecules.