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

06 April 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


After two decades of continued development of the Martini coarse-grained force field (CG FF), further refining the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically-designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept here we employ SwarmCG, an automatic multi-objective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid & bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), respectively informing on the supra-molecular structure of the lipid bilayer systems and on their sub-molecular dynamics. In our training sets 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 (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ~80 model parameters within still limited computational budgets, we show that this protocol allows to obtain improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine tuning of the representation and parameters of the models may improve their accuracy, and how automatic approaches such as SwarmCG may be very useful to this end.


molecular dynamics
reinforcement learning
molecular modeling

Supplementary materials

Supplementary Material - On the Automatic Optimization of Lipid Models in the Martini Force Field using SwarmCG
Supplementary material includes details on the functional form of the CG FF within which the parameters are optimized, the molecular models used in these experiments, their topologies, their optimized FF parameters obtained with SwarmCG in the context of Representations 1 and 2, as well as the implementation for usage with HPC resources. Additional details are also provided concerning the submolecular features observed in the CG models obtained at the end of the optimization experiments, underlining the relevance of the OT-B metrics.


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