We examine the performance of the trained force fields on the training set, as well as the changes in parameters after optimizations. This detailed look at the optimization sheds light on which parameter changes are driving the specific property improvements that result in an improved force field. Using the RMSE for each target property as a metric and grouping by property and chemical environment, it is clear that most of the different moieties in the training set are improving when trained against either pure or mixture data. This is evident when training against both the "pure only" data set in Figure and the "mixture only" data set in Figure . Improvements in both pure and mixture training data for the other two (mixed) optimizations were also observed, which are shown in supporting information (Section 2.6.2,2.7.2). (right panel) measurements in the "pure only" training set, estimated using the initial parameters (OpenFF 1.0.0, blue points) and the final parameters after 12 optimization iterations ("pure only", orange points). RMSEs are categorized by chemical environment, and error bars represent 95% confidence intervals computed by bootstrapping with replacement for 1000 iterations. The results from the other training sets containing pure properties ("mixtures and pure density", "pure and mixture") are statistically equivalent, with the exception of ketone pure densities (statistically better in the "pure only" set), and alcohol heats of vaporization (statistically inferior in the "pure only" dataset). Figures for other optimization are available in Supporting Information Section 2.6.2,2.7.2. One notable exception is ketones, as pure ketone densities and "Ketone > Ether" binary densities were both degraded upon training. Given that this occurs for both pure and mixture training data, it is unlikely that it is a symptom of the training sets selected. We also note that ketone Δ RMSEs are improved, alongside both densities and Δ RMSEs for esters, which utilize the same [#8:1] generic carbon param-eter. It is likely that these properties are improved at the expense of ketone densities. By examining the first derivatives of the density contribution to the objective function with respect to the force field parameters, again partitioned by moiety (Figure ), we see that modifying the [#1:1]-[#6X4] (hydrogen attached to tetravalent carbon), [#6X4] (tetravalent carbon), and [#8:1] (generic oxygen) has an opposite effect on ketone objectives compared to the objective for other moieties. This suggests that the force field lacks the degrees of freedom required to accurately capture carbons and hydrogens in ketone environments alongside the other environments represented by the same SMIRKS patterns. It is possible that including a more specific hydrogen or carbon parameter for this environment might improve prediction of ketone densities. Another possibility is that the LJ parameters are compensating for deficiencies in the AM1-BCC electrostatic model, which was not optimized in this study. This result will be explored in further work as it is beyond the scope of the current study. However, analyses such as these point out how additional interaction types can be motivated by the large sets of data generated by this sort of study. ). This suggests that adding a separate parameter (or parameters) to explicitly address ketone environments is likely to improve parameterization.