The other objective of our ML-guided DoE was to optimally space-fill so that we can develop predictive surrogate models over the entire formulations design space. We already established that we fixed the polymer and thickener for a particular sub-system and explored all these subsystems, therefore, we look at the spread of surfactants used in Figure . We prepared a total of 384 formulations for the three difficult-to-formulate sub-systems, as identified earlier, and 438 further samples for the remaining five sub-systems. The dashed lines in Figure show the expected number of samples per surfactant if we had uniformly sampled the ingredients. We observe that for the purely space-filling designs, our surfactants' distribution is very close to this expected value, showing excellent space-filling properties. By comparison, and as we would expect, we have a non-uniform distribution for the stability-guided experiments as our classifier learned that certain surfactant(s) would lead to unstable results with a particular polymer, thickener, or indeed, another surfactant. For example, as stated earlier 𝑃 ! and 𝑃 " are highly charged cationic polyelectrolytes, so Texapon ® SB 3 KC, Plantapon ® ACG 50, and Plantapon ® LC7, which are anionic surfactants would often form coacervates with these ingredients, and therefore you see they are under sampled for the stability-guided designs. And following this argument, Dehyquart ® A-CA, the only cationic surfactant in the set, was particularly favoured for the stability-guided experiments. Despite this, we have still sampled all the ingredients relatively well, which was achieved through modifying the phase stability cut-off to balance exploration of our design space, which leads to a more space-filling design, and simultaneously exploiting our classifier to drive the experiments to feasible regions of the design space. Looking at the selection of surfactants, polymer, and thickener covers the qualitative variables in our design problem; in Figure we show the coverage of our quantitative design variables -ingredient concentrations (w/w%). As stated in Step (2) of Scheme 1, we aimed for the surfactants to have a distribution between 8 -13 w/w%, conditioning polymers 1 -3 w/w%, and thickeners 1 -5 w/w%. We observe a good distribution of concentrations across all the ingredients, where the median and interquartile ranges (IQR) are given by the dashed lines on the violin plots in Figure . Therefore, our full formulations design space has been represented in the generated dataset. We only note that for some ingredients, namely the very viscous ones, we exceeded the suggested concentration bounds for experimental reasons, however, this is acceptable and still informative towards developing property prediction models. Where the target ranges have been exceeded this is due to viscous liquid handling challenges.