Data-driven identification and analysis of the glass transition in polymer melts

TL;DR

This paper introduces a data-driven method combining molecular dynamics, PCA, and clustering to accurately identify the glass transition temperature in polymer melts from short simulation trajectories.

Contribution

It presents a novel approach that leverages structural and dynamical data to determine the glass transition temperature, even with limited simulation time.

Findings

Successfully identifies glass transition temperature in polymer melts.

Reveals changes in chain behavior across the transition.

Applicable to other polymeric glass-forming liquids.

Abstract

Understanding the nature of glass transition, as well as precise estimation of the glass transition temperature for polymeric materials, remain open questions in both experimental and theoretical polymer sciences. We propose a data-driven approach, which utilizes the high-resolution details accessible through the molecular dynamics simulation and considers the structural information of individual chains. It clearly identifies the glass transition temperature of polymer melts of weakly semiflexible chains. By combining principal component analysis and clustering, we identify the glass transition temperature in the asymptotic limit even from relatively short-time trajectories, which just reach into the Rouse-like monomer displacement regime. We demonstrate that fluctuations captured by the principal component analysis reflect the change in a chain's behaviour: from conformational rearrangement above to small rearrangements below the glass transition temperature. Our approach is straightforward to apply, and should be applicable to other polymeric glass-forming liquids.