As demonstrated, automated integrative modeling approaches such as SwarmCG can be successfully leveraged not only for calibrating force field parameters, but also for (in)validating modeling philosophies, with higher throughput and enhanced certainty than what can be done manually. To this end, here we relied principally on experimental APL and Dʜʜ thickness values for optimizing the bonded parameters of PC lipid models, iteratively simulating small patches of bilayers at a limited number of temperatures across phase states. We note, however, that this protocol could be extended with limited efforts to obtain bonded building blocks for a much larger set of lipid models in the context of Martini 4 . The training set can be further enriched by including additional bilayer systems (e.g. different lipid head types, sphingomyelins, etc.) and the loss function can directly evaluate also other experimental measurements providing transversal information (e.g. the hydrocarbon thickness would complement well the currently employed Dʜʜ thickness and APL). Other available experimental properties such as bending modulus or diffusion constants may also be exploited at a slightly higher computational cost. Using annealing simulations directly in the training set could also enable finer estimation and tuning of the gel/liquid phase transition temperatures.