Many-Body Dissipative Particle Dynamics with the MARTINI "Lego" approach

TL;DR

This paper extends the MARTINI coarse-grained force-field's Lego approach to many-body dissipative particle dynamics (MDPD), enabling faster simulations of soft matter systems while maintaining accuracy, demonstrated through lipid bilayer comparisons.

Contribution

It introduces a novel application of the MARTINI Lego approach within MDPD, significantly increasing simulation speed for soft matter systems.

Findings

MDPD with MARTINI Lego approach is 4-7 times faster than traditional MD.

The lipid bilayer properties are accurately reproduced using the new method.

The approach broadens the applicability of coarse-grained simulations in soft matter research.

Abstract

MARTINI is a popular coarse-grained force-field that is mainly used in molecular dynamics (MD) simulations. It is based on the ``Lego'' approach where intermolecular interactions between coarse-grained beads representing chemical units of different polarity are obtained through water--octanol partition coefficients. This enables the simulation of a wide range of molecules by only using a finite number of parametrized coarse-grained beads, similar to the Lego game, where a finite number of bricks are used to create larger structures. Moreover, the MARTINI force-field is based on the Lennard-Jones potential with the shortest possible cutoff including attractions, thus rendering it very efficient for MD simulations. However, MD simulation is in general a computationally expensive method. Here, we demonstrate that using the MARTINI ``Lego'' approach is suitable for many-body dissipative particle (MDPD) dynamics, a method that can simulate multi-component and multi-phase soft matter systems in a much faster time (about 4--7 times) than MD. In this study, a DPPC lipid bilayer is chosen to provide evidence for the validity of this approach and various properties are compared to highlight the potential of the method. Thus, we anticipate that our study opens new possibilities for faster simulations of a wide range of soft matter systems by using the MDPD method.