We have presented QM9-Solvation, a new dataset of DFT-computed solvation energies of 130 258 molecules in five solvents, a total of 651 290 values, and used this new dataset to train a suite of machine learning models. Our best-performing machine learning models, based on a message-passing neural network that takes properties of both solute molecules and solvent as inputs, achieve an accuracy of 0.5 kcal/mol, 2× better than group contribution models.