Since the optimizations find a large number of separate and diverse solutions, and the set of parameters is small enough to be reasonably interpretable, it is worth examining some of the parameter changes to understand their physical basis and inform future optimizations. Here we analyze some of the most notable changes from the 𝑁 = 10 replicates, which had larger parameter changes relative to the 𝑁 = 5 replicates. The change in parameters from the original OpenFF 1.0.0 is shown in Figure . To identify what points in the training dataset the parameter changes are affecting, we examine the bias of the physical properties training dataset, as measured by mean signed error (MSE). The bias before and after training, as a function of chemical group, is plotted for multi-fidelity run 1, shown in Figure . Run 1 is shown as an example as it has one of the best objective functions and no unusual parameter changes (such as occurred in runs 3 and 4), and MSE values are similar for all optimization runs. Similar plots of RMSE and MSE for all 5 runs are available in the Supporting Information, Section S2.2. The 𝜖's for [#6:1] and [#8:1] present an interesting case in multidimensional optimization. We see significant changes in the 𝜖's for both [#6:1] and [#8:1], which represent generic hydrogens and oxygens. However, the presence of more specific types in the training set means that these two types are only exercised together in a C=O double bond (a ketone, ester, or carboxylic acid). In all optimizations but run 3, we see a large reduction in [#6:1] 𝜖 and a slight increase in [#8:1] 𝜖. The adjustment of these parameters, along with an increase in the [#6:1] 𝑅 𝑚𝑖𝑛∕2 , corrects an overprediction in the ester and ketone Δ𝐻 𝑣𝑎𝑝 . Notably, simulation-only optimization against the same training set was not able to achieve the same correction for esters.