Earlier works investigating polymeric systems typically considered a confined range of χ-parameters to understand the nature of their phase diagram. In subsequent works targeting biological lipids, the χ-parameters were chosen by a combination of inference from MARTINI Lennard-Jones parameters and by-hand adjustments in order to reproduce all-atom density profiles. To systematize the parameterization of χ-parameters, some of us proposed an automated machine-learning procedure. There, similar to the self-driving labs approach in the Guzik Group, Bayesian Optimization (BO) was used to run simulation experiments that compute density profiles for different χ-values, determining the optimal χ-parameters that maximize the agreement with all-atom reference profiles.