arXiv subjects

Nguyen T. T. Duyen

Publications and source records attributed to Nguyen T. T. Duyen.

3 recordsLinked to original sources

arXiv2026

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

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.

cond-mat.soft↗

arXiv2025

Predicting Structural Relaxation in Supercooled Small Molecules via Molecular Dynamics Simulations and Microscopic Theory

Understanding and predicting the glassy dynamics of small organic molecules is critical for applications ranging from pharmaceuticals to energy and food preservation. In this work, we present a theoretical framework that combines molecular dynamics simulations and Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to predict the structural relaxation behavior of small organic glass-formers. By using propanol, glucose, fructose, and trehalose as model systems, we estimate the glass transition temperature (Tg) from stepwise cooling simulations and volume-temperature analysis. These computed Tg values are then inserted into the ECNLE theory to calculate temperature-dependent relaxation times and diffusion coefficients. Numerical results agree well with experimental data in prior works. This approach provides a predictive and experimentally-independent route for characterizing glassy dynamics in molecular materials.

cond-mat.soft↗

arXiv2025

Bridging Machine Learning and Glassy Dynamics Theory for Predictive Polymer Modeling

Understanding and predicting the glassy dynamics of polymers remain fundamental challenges in soft matter physics. While the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory has been successful in describing relaxation dynamics, its practical application to polymers depends on a thermal mapping to connect theory with experiment, which in turn requires detailed thermodynamic data. Such data may not be available for chemically complex or newly designed polymers. In this work, we propose a simple approach that integrates machine learning-predicted glass transition temperatures (Tg) with a simplified thermal mapping based on an effective thermal expansion coefficient to overcome these limitations. This approach can provide quantitatively accurate predictions of relaxation dynamics across a broad range of polymers. Rather than replacing the original thermal mapping, our method complements it by trading formal rigor for computational efficiency and broader applicability in high-throughput screening and materials with limited available data. Moreover, we introduce a physically motivated modification to the thermal mapping that resolves discrepancies in the description of low-Tg polymers. Our results establish a generalizable approach for predictive modeling of glassy polymer dynamics and point toward new directions for theory-guided materials discovery.

cond-mat.soft↗