This work resulted in a number of key findings which will inform future work. In particular, lead-up work suggested that the choice of condensed phase property data used in training LJ parameters is particularly important, and we found that using mixture properties rather than pure solution properties and heat of vaporization seemed to improve performance. In Sage, this manifested by significantly improved performance on solvation free energies for both aqueous and non-aqueous solvents (Fig. ). We also observed that the choice of quantum chemical dataset for valence parameters substantially impacted performance, and the dataset expansions for Parsley 1.2.0, 1.3.0 and Sage improved performance here substantially relative to Parsley 1.0.0. We also increased the size and diversity of our quantum chemical benchmarking datasets, allowing us both to better see changes in overall performance and identify specific chemistries and parameters that need further refinement.