Data-driven identification and analysis of the glass transition in polymer melts
This work addresses the open challenge of precisely estimating glass transition temperatures in polymer science, offering a straightforward method that could be applied to other polymeric liquids, though it is incremental as it builds on existing simulation techniques.
The researchers tackled the problem of identifying and analyzing the glass transition in polymer melts by developing a data-driven method that uses molecular dynamics simulations and structural chain information, successfully identifying the glass transition temperature for weakly semiflexible chains from short-time trajectories.
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.