We further explored the concept of multi-solvent prediction by withholding data from one of our five solvents, ACN, to use as a test set. We selected ACN for this test case because it has the median dielectric constant of the five solvents and, thus should be a good test for our machine learning model's ability to interpolate. The MAE of our dielectric-constant model for predicting solvation energy in water is not changed by the exclusion of ACN training data; the MAE decreases by only 5 × 10 -4 kcal/mol. Based on these results, we see neither benefit nor disadvantage to training a machine learning model on data from more solvents at least for solvents that exist within the range of dielectric constants in our training set.