Data-driven identification and analysis of the glass transition in polymer melts
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We propose a data-driven approach based on information about structural fluctuations of polymer chains, which clearly identifies the glass transition temperature T_g of polymer melts of weakly semiflexible chains. We use principal component analysis (PCA) with clustering to distinguish between liquid and glassy states and predict T_g in the asymptotic limit. Our method indicates that for temperatures approaching T_g from above it is sufficient to consider short molecular dynamics simulation trajectories, which just reach into the Rouse-like monomer displacement regime. The first eigenvalue of PCA and participation ratio show sharp changes around T_g. Our approach requires minimum user inputs and is robust and transferable.
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