Graph-Network-Based Predictive Modeling for Highly Cross-Linked Polymer Systems

TL;DR

This paper introduces a graph-network-based algorithm that predicts the evolution of highly cross-linked polymer structures during polymerization, integrating molecular dynamics simulations to handle complex chemical and physical changes.

Contribution

The study presents a novel, adaptable graph-network algorithm that accurately models polymerization in highly cross-linked systems, bridging chemical bond formation with physical property prediction.

Findings

Successfully applied to various amorphous polymers including PPNs and epoxy-resins

Enables rapid prediction of structural and material property changes during polymerization

Integrates with LAMMPS for comprehensive molecular dynamics simulations

Abstract

In this study, a versatile methodology for initiating polymerization from monomers in highly cross-linked materials is investigated. As polymerization progresses, force-field parameters undergo continuous modification due to the formation of new chemical bonds. This dynamic process not only impacts the atoms directly involved in bonding, but also influences the neighboring atomic environment. Monitoring these complex changes in highly cross-linked structures poses a challenge. To address this issue, we introduce a graph-network-based algorithm that offers both rapid and accurate predictions. The algorithm merges polymer construction protocols with LAMMPS, a large-scale molecular dynamics simulation software. The adaptability of this code has been demonstrated by its successful application to various amorphous polymers, including porous polymer networks (PPNs), and epoxy-resins, while the algorithm has been employed for additional tasks, such as implementing pore-piercing deformations and calculating material properties.