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Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as

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Diffusion maps (DM) and Gaussian Mixture Models (GMMs) effectively determine the glass transition temperature (Tg) in polymers like PLA and PHB from molecular dynamics simulations, outperforming principal component analysis (PCA).

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Physics

Background:

  • Machine learning offers advanced pattern recognition in complex datasets.
  • Understanding the glass transition temperature (Tg) is crucial for polymer performance.
  • All-atom molecular dynamics simulations provide detailed insights into polymer behavior.

Purpose of the Study:

  • To evaluate the glass transition temperature (Tg) using machine learning on molecular dynamics simulations.
  • To compare the efficacy of Principal Component Analysis (PCA) and Diffusion Maps (DM) for Tg determination.
  • To explore various molecular descriptors for capturing polymer state transitions.

Main Methods:

  • Employed all-atom molecular dynamics simulations for polylactide (PLA) and poly(3-hydroxybutyrate) (PHB).
  • Utilized Principal Component Analysis (PCA) and Diffusion Maps (DM) for dimensionality reduction.
  • Applied Gaussian Mixture Models (GMMs) to analyze low-dimensional representations and quantify log-likelihoods.
  • Calculated Tg by observing the overlap of log-likelihood distributions during simulated cooling.

Main Results:

  • Diffusion Maps (DM) with radial distribution functions (RDF) and mean square displacements (MSDs) accurately predicted Tg for PLA and PHB, matching simulation data.
  • DM-transformed dihedral angle (DA) and relative square displacement (RSD) data yielded Tg values consistent with experimental findings.
  • Principal Component Analysis (PCA) showed less reliable Tg predictions compared to DM across the tested descriptors.
  • A clear separation into melt and glass states was identified using GMMs on PCA and DM projections.

Conclusions:

  • The combination of atomistic simulations and Diffusion Maps (DM) with Gaussian Mixture Models (GMMs) provides a robust framework for calculating Tg.
  • DM demonstrates superior performance over PCA in predicting Tg from molecular dynamics data for the studied polymers.
  • This integrated approach offers a unified method for studying the glass transition across diverse molecular descriptors in glass-forming materials.