Mesoscopic modelling of bio-compatible PLGA polymers with coarse-grained molecular dynamics simulations

TL;DR

This paper introduces a coarse-grained molecular dynamics model for bio-compatible PLGA polymers that accurately predicts physical properties, enabling larger scale simulations closer to experimental conditions.

Contribution

The paper presents a novel CG model for PLGA polymers using ellipsoids and generalized potentials, validated against atomistic data for the first time.

Findings

Quantitative agreement with atomistic models for key properties

Model enables larger, more realistic simulations

Potential for broader application to other polymers

Abstract

A challenging topic in materials engineering is the development of numerical models that can accurately predict material properties with atomistic accuracy, matching the scale and level of detail achieved by experiments. In this regard, coarse-grained (CG) molecular dynamics (MD) simulations are a popular method for achieving this goal. Despite the efforts of the scientific community, a reliable CG model with quasi-atomistic accuracy has not yet been proposed for the design and prototyping of materials, especially polymers. In this paper we describe a CG model for polymers, focusing on the bio-compatible poly(lactic-co-glycolic acid) (PLGA), based on a general parametrization strategy with a potentially broader field of applications. In this model, polymers are represented with finite-size ellipsoids, short-range interactions are accounted for with the generalized Gay-Berne potential, while electrostatic and long-range interactions are accounted for with point charges within the ellipsoids. The model was validated against its atomistic counterpart, obtained through a back-mapping process, by comparing physical properties such as glass transition temperature, thermal conductivity, and elastic moduli. We observed quantitative agreement between the atomistic and CG representations, thus opening up the possibility of adopting the proposed model to expand the domain size of typical MD simulations to dimensions comparable to those of experimental setups.