2. The nature of mixture data allows users to more easily include a diverse spectrum of interactions in their training sets. For example, mixtures of drug-like molecules with pharmaceutically relevant solvents or amino acid analogues can in principle be readily included in training sets to allow the LJ parameters of solvents, ligands, and bio-polymers to be self-consistently trained. 3. Although computing some properties of mixtures may require multiple simulations, most such properties (including those studied here) do not require simulations in different phases, minimizing error caused by polarization differences. There may be some difference in polarization of molecules between more polar and less polar liquids, but this difference is significantly less than the difference between two phases, especially since liquid mixtures are, by definition, miscible and the components must therefore have dielectric constants that are not completely dissimilar. 4. Including mixture data adds the ability to vary training set data by composition; data points can be selected at ( , , ) rather than just ( , ), probing the balance between pure and mixture interactions. 5. Many data points for mixture properties are available in modern sources such as the ThermoML Archive. In particular, binary Δ ( ) measurements are much more abundant in the ThermoML archive compared to pure Δ . For the moieties and conditions of interest in our study, there are 382 binary mixtures with Δ ( ) measurements (generally available at multiple concentrations), compared to 24 single-component Δ measurements that fit the same criteria. For density measurements, both mixture and pure component data points are relatively abundant, with 4000 data points for pure substances and 900 binary mixtures matching our criteria. We estimate that there is sufficient binary mixture training data to parameterize small molecules containing carbon, hydrogen, oxygen, nitrogen, chlorine, and bromine.