Therefore, we used and present a weighted-space filling design to guide our experiments to regions of phase stability. However, we do not know a priori which regions of the formulations design space will be stable. Therefore, we used an active learning approach to train a predictive phase stability classifier across the design space, which would be used to guide difficult-toformulate sub-systems to regions of stability, as part of a machine learning-guided DoE (MLguided DoE). Here the DoE component of the method was developed using the MaxProQQ package. With this study, we show that we are able to optimise for the chemical diversity and phase stability in our formulations dataset.