Computational framework for polymer synthesis to study dielectric properties using polarizable reactive molecular dynamics

TL;DR

This paper introduces a scalable computational framework using reactive molecular dynamics with a polarizable charge model to accurately predict dielectric properties of polymers, aiding the discovery of high energy density materials.

Contribution

It presents a novel, efficient computational method that incorporates morphology and temperature effects to predict dielectric properties with near-quantum accuracy.

Findings

Framework successfully predicts dielectric properties of high energy density polymers.

Demonstrates capability to handle relevant polymer morphologies.

Enables high-throughput screening for new polymer materials.

Abstract

The increased energy and power density required in modern electronics poses a challenge for designing new dielectric polymer materials with high energy density while maintaining low loss at high applied electric fields. Recently, an advanced computational screening method coupled with hierarchical modelling has accelerated the identification of promising high energy density materials. It is well known that the dielectric response of polymeric materials is largely influenced by their phases and local heterogeneous structures as well as operational temperature. Such inputs are crucial to accelerate the design and discovery of potential polymer candidates. However, an efficient computational framework to probe temperature dependence of the dielectric properties of polymers, while incorporating effects controlled by their morphology is still lacking. In this paper, we propose a scalable computational framework based on reactive molecular dynamics with a valence-state aware polarizable charge model, which is capable of handling practically relevant polymer morphologies and simultaneously provide near-quantum accuracy in estimating dielectric properties of various polymer systems. We demonstrate the predictive power of our framework on high energy density polymer systems recently identified through rational experimental-theoretical co-design. Our scalable and automated framework may be used for high-throughput theoretical screenings of combinatorial large design space to identify next-generation high energy density polymer materials.