Large simulation data sets naturally lend themselves to the analysis by machine learning, underpinning recent breakthroughs . As the parameter spaces are large, active learning where ML models help to prioritize what simulations to run and can help to derive sequence-property relations , e.g. to predict phase separation concentrations or design sequences ]. In addition, combining active learning with a mean-field interaction model can enable the prediction of co-phase separation propensities .