The only example from Figure where our ML-guided DoE fails to increase the stable in round (%) is after the first round of experiments with (𝑃 ! , 𝑇 ! ). We go from 29 to 13% of formulations being stable in round 1 vs. 2. However, this can be clearly explained as shown by Table in the SI. Tables provide the full set of phase stability-guided DoE results for all three sub-systems, complementary to Figure . Initially, we chose to apply a cut-off as some top x% of experiments. On round one of (𝑃 ! , 𝑇 ! ), which was the first sub-system we explored, we restricted the candidate set to the top 20% of stable predicted experiments; however, this cutoff was equivalent to a 0.29 phase stability threshold, which would still include a majority of unstable formulations, as seen in round two. We, therefore, soon switched to only defining the phase stability cut-off as a predicted probability of stability between zero and one, so we could more clearly control the degree of stability tuning. Additionally, as seen in Table , the best classifier at round one had ROC AUC and 𝐹 ! scores of 0.62 and 0.73, respectively, which corresponds to a moderately predictive classifier. As highlighted above, it is essential to be able to develop a highly predictive classifier, otherwise, we cannot apply this weighted search strategy effectively. By contrast, the initial classifiers trained for (𝑃 ! , 𝑇 " ) and (𝑃 $ , 𝑇 ! ) are excellent (see Tables and), and so we could successfully guide our difficult-to-formulate sub-systems to regions of stability in just one or two iterations.