Multiscale Prediction of Polymer Relaxation Dynamics via Computational and Data-Driven Methods

TL;DR

This paper introduces a multiscale modeling framework combining molecular dynamics, machine learning, and ECNLE theory to predict polymer glass transition and relaxation dynamics, validated against experimental data.

Contribution

It develops an integrated computational and data-driven approach for accurately predicting polymer relaxation behavior and glass transition temperatures.

Findings

Predicted Tg values align well with experimental data.

Machine learning slightly overestimates Tg but maintains accurate fragility.

ECNLE calculations agree with dielectric spectroscopy results.

Abstract

We present a multiscale modeling approach that integrates molecular dynamics simulations, machine learning, and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of polymer systems. The glass transition temperatures (Tg) of four representative polymers are estimated using simulations and machine learning model trained on experimental datasets. These predicted Tg values are used as inputs to the ECNLE theory to compute the temperature dependence of structural relaxation times and diffusion coefficients, and the dynamic fragility. The Tg values predicted from simulations show good quantitative agreement with experimental data. While machine learning tends to slightly overestimate Tg, the resulting dynamic fragility values remain close to experimental fragilities. Overall, ECNLE calculations using these inputs agree well with broadband dielectric spectroscopy results. Our integrated approach provides a practical and scalable tool for predicting the dynamic behavior of polymers, particularly in systems where experimental data are limited.