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

On cooling, the dynamics of polymer melts slow down exponentially, leading to a glassy state without any drastic change in static structure. We employ data-driven approaches based on information about purely structural fluctuations of the chains to identify the glass transition from coarse-grained weakly semi-flexible polymer model simulations. More precisely, we used principal component analysis (PCA) to quantify conformational fluctuations and identify a sharp change in fluctuations around the glass transition temperature. The first PCA eigenvalue and the participation ratio show clear signatures of glass transition. The proposed method of glass transition temperature prediction is less ambiguous compared to the existing methods, as it requires minimal prior knowledge about the system and user inputs.