Benchmarking simulations of the test set physical property measurements were performed for OpenFF 1.0.0 and each of the refitted force fields. Mean shift and and shift distributions (metrics described in the Methods section) for the test sets of each of the four physical properties used in training are shown in Figure . The mean shift of Δ absolute errors relative to OpenFF 1.0.0 show that training against the "mixture only" set provides an improvement over the initial force field, whereas training against the "pure only" set degrades Δ predictions. This is also reflected in the KDE plot, where the peak of the shift distribution is shifted right for the two sets that contain Δ ("pure only", "pure and mixture") compared to the two that do not ("mixture only" and "mixtures + pure density"), suggesting that refitting to Δ hinder attempts to reproduce properties like Δ . It is important to note that the initial LJ parameters used in this force field were fitted to Δ simulation when originally determined[106? ], and that the RMSE of OpenFF 1.0.0 on the Δ test set is 3.3 kJ/mol, so reasonably accurate predictions can be obtained with LJ parameters trained against Δ . However, training against mixture data can offer additional improvements to performance. These results indicate that mixture properties can replace physical properties of pure systems as a target for training LJ parameters, particularly in cases where more and more chemically diverse data is available for mixtures. Training against the "pure only" set does lead a significant improvement to Δ ( ) against the baseline; however, training directly against the "mixture only" set yields a much larger improvement. It appears that training against properties of mixtures alone sufficiently constrains the optimization, and includes enthalpic information that the traditional pure dataset alone does not. We also note that augmenting a traditional pure data training set with mixture data (such as the "pure and mixture" set) can improve treatment of mixture properties without degrading performance on pure properties.