Automatic Multi-Objective Optimization of Coarse-Grained Lipid Force Fields Using SwarmCG

TL;DR

This paper introduces SwarmCG, an automatic multi-objective optimization method for developing transferable coarse-grained lipid force fields that align with both experimental data and high-resolution simulations.

Contribution

It presents a novel multi-objective optimization framework that enhances transferability of CG force fields using combined experimental and simulation data.

Findings

SwarmCG successfully optimizes lipid force fields matching experimental data.

The method improves transferability across different lipid bilayer types.

Insights into the balance between model precision and resolution were obtained.

Abstract

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.