Notably, training against the mixture properties appears to have corrected a systematic error in the enthalpy of mixing, which training against pure properties alone is not able to correct. This can be inferred from the KDE plot for Δ ( ) in Figure , where the shift of the secondary peak indicates that performance is improving for a subset of molecules. More specific evidence is obtained from a simulation/experiment parity plot for Δ ( ), where a systematic underprediction of alcohol/ester (green points) and alcohol/ketone (orange points) mixture enthalpies is corrected (Figure ). The improvement in the treatment of alcohol/ketone mixtures was achieved without directly including these mixtures in the training set. This is particularly significant as alcohol/ester and alcohol/ketone mixture enthalpies have strong deviations from ideal solution behavior. Namely, ketone and esters are both hydrogen bond acceptors only and thus do not form hydrogen bonds in the pure phase. However when mixed with a hydrogen bond donor (an alcohol) they do. This change is likely related to the reduction in for the [#8X2H1+0:1] (hydroxyl oxygen), noted in Figure . This reduction is much larger (1.7%-2.8% vs. 0.4% for "pure only") for force fields refit against mixture data. This is where mixture properties, and especially their ability to more readily capture complementary interactions, appear to be advantageous over pure properties.