Using our automatic data set selection and force field optimization workflow, we re-parameterized select LJ parameters of the OpenFF 1.0.0 force field against training sets containing combinations of pure ( , Δ ) and mixture ( ( ), Δ ( )) properties for alkanes, alcohols, esters, ethers, ketones, and acids. These training sets were controlled such that the same molecules are used in both pure and mixture training sets, to isolate the effect of the different data types used. Through iterative optimization of parameter sets, new force fields were produced that all exceeded the performance of the initial force field on some parts of the test set. Furthermore, we observe that training LJ parameters against mixture data constrains the optimization in a comparable or superior manner to optimizing with the traditional pure properties commonly used in LJ parameterization.