Our next step was to add the ability for our MPNN model to predict solvation energies in other solvents. We explored two different strategies for creating "multi-task" models able to predict solvation energy in different solvents simultaneously. The first approach is a standard multi-task model in which the last layer of the neural network produces multiple outputs, one per solvent. The second approach involves a network that predicts solvation energy given the molecular structure of the solute and the dielectric constant of the solvent. This "dielectric constant" model, unlike the multi-task model, can predict solvation energy in solvents not included in the training set. Here, we test which method produces more accurate models We evaluate our two strategies by training a model with the same 90%/10% test split as our single-solvent model and data from all five solvents. The multi-task and dielectricconstant models have comparable accuracy to the single-solvent model in predicting solvation energy in water. The single-solvent model has an error of 0.44 kcal/mol for solvation energy in water-effectively equal to the errors of 0.43 and 0.45 kcal/mol for the multi-task and dielectric constant models, respectively. We note that we previously observed that training a multi-task model with greatly varied properties (e.g., HOMO energy and atomization energy) can lead to a significant degradation in model accuracy. It appears that the solvation energies of a molecule in different solvents are sufficiently similar that modeling different solvents simultaneously in the same model does not lead to degradation in performance.