Interestingly, the parameter trajectories in the 𝑁 = 10 optimizations explore a wider range of parameters than the 𝑁 = 5 optimizations, as shown in Figure . One might expect that an optimizer with less information would take larger jumps as it attempts to find a minima, but it is likely here that optimization with a less specified initial surrogate (𝑁 = 5) proposes more solutions that are rejected and therefore finds more conservative minima. Particularly in the oxygen parameters for runs 3 and 4, we can see that this optimization setting can make some very large parameter changes; run 3 has the lowest overall objective, but more than doubles the hydroxyl oxygen 𝜖. This may suggest that adding some regularization could benefit the transferability of the optimization, but it also reflects that the ratio of optimization targets to inputs leads to an optimization where many solutions can be found.