. Training against Δ is problematic due to limited data coverage and quality, as well as changes in molecular polarization between liquid and gas phase simulations. Mixture property datasets also offer expanded datasets by varying composition, and are more widely available in the ThermoML Archive. Moreover, we have shown here how mixture properties offer significant advantages over pure properties as an optimization target, especially in those cases of interactions which deviate strongly from ideality. These advantages lead to improved LJ parameters sets and better agreement with experiment. Given that we control for the identity of the molecules in the training set, this demonstrates that mixture properties contain information about intermolecular interactions that pure component property measurements do not.