  The development of coarse-grained (CG) molecular models typically requires a
time-consuming iterative tuning of parameters in order to have the approximated
CG models behaving correctly and consistently with, e.g., available
higher-resolution simulation data and/or experimental observables. Automatic
data-driven approaches are increasingly used to develop accurate models for
molecular dynamics simulations. But the parameters obtained via such automatic
methods often make use of specifically-designed interaction potentials, and are
typically poorly transferable to molecular systems or conditions other than
those used for training them. Using a multi-objective approach in combination
with an automatic optimization engine (SwarmCG), here we show that it is
possible to optimize CG models that are also transferable, obtaining optimized
CG force fields (FFs). As a proof of concept, here we use lipids, for which we
can avail of reference experimental data (area per lipid, bilayer thickness)
and reliable atomistic simulations to guide the optimization. Once the
resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the
parameters of the CG lipid models iteratively and simultaneously against
higher-resolution simulations (bottom-up) and experimental data (top-down
references). Including different types of lipid bilayers in the training set in
a parallel optimization guarantees the transferability of the optimized lipid
FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement
with experimental data for different resolution CG FFs. We also obtain
stimulating insights on the precision-resolution balance of the FFs. The
approach is general and can be effectively used to develop new FFs, as well as
to improve existing ones.
