However, this is a big data problem, a luxury we are not afforded when working with formulations, therefore, this highlights the continued importance of the formulator's expertise to suggest suitable concentration bounds for us to explore within. Henceforth, we exclude subsystems (𝑃 " , 𝑇 ! ) and (𝑃 " , 𝑇 " ) from our results for difficult-to-formulate sub-systems, as we could not apply the weighted-space filling design without a predictive stability classifier. We, therefore, had three sub-systems for which our ML-guided DoE was used, as shown in Figure . For the rest, a purely space-filling MaxProQQ design was used throughout. We highlight, with the exception of (𝑃 $ , 𝑇 ! ), the difficult-to-formulate sub-systems were primarily those prepared with the highly charged cationic polyelectrolytes, 𝑃 ! and 𝑃 " because these would often form coacervates with the anionic surfactants in our set of ingredients.