On the Automatic Optimization of Lipid Models in the Martini Force Field using SwarmCG

After two decades of continued development of the Martini coarse-grain force field, further refining Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Here we employ SwarmCG, an automatic multi-objective optimization approach facilitating the development of lipid force fields, to calibrate exclusively the bonded parameters of lipid models in the context of the finely tuned non-bonded interaction matrix available in Martini 3.0.0. We explore two different CG representations of the molecules and evaluate their ability to further enhance the thermodynamic properties of lipid models in Martini simulations. As training set, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers, composed of phosphatidylcholine lipids spanning various tail lengths and degrees of unsaturation. We evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture, thereby gaining insights on the ability of putative refined CG representations to further enhance lipid models in Martini.