The binary mixtures selected for the mixture training set (Figure ) are composed of the molecules included in the pure training set, and were manually chosen to include a diverse set of interactions. These property measurements were sourced directly from the NIST ThermoML archive using the OpenFF Evaluator's built-in data selection tools. Where available, three ( ) and three Δ ( ) data points were included for each binary mixture, one each at 25%, 50%, and 75% composition, or as close to these values as possible given data availability. These compositions were chosen so as to ensure that the set included both components in excess to the other as well as in close to equal amounts. Compositions between 25-75% should capture most of the relevant information, as deviations from ideality for many mixtures are maximized near an equal mixture. Mixtures with compositions close to pure (e.g. > 0.9) were excluded, as when the concentration of one component becomes small, our simulation boxes (1000 total molecules) would have a very low number of molecules of that component. In total, measurements made for 33 binary mixtures were selected for a total of 195 data points. This is significantly more than the 56 total data points in the pure data set, but it is drawn from a number of mixtures similar to the number of compounds in the pure training set. We note that after training was complete, we discovered that one Δ ( ) data point in the mixture training set was transcribed into ThermoML incorrectly (described in Supporting Information, Section 4). The mixture data used in our training sets contains one ( ) and one Δ ( ) measurement per mixture for three different compositions if multiple compositions were available (close to 25%, 50% and 75%) measured at close to ambient conditions (P=1 atm, T=298 K), yielding a training set of 33 binary mixtures with 187 data points total.