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James L Suter1, Werner A Müller1, Maxime Vassaux2

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|January 29, 2025
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We developed a faster computational method to predict polymer glass transition temperature (Tg) using molecular dynamics (MD). This ensemble approach reduces computation time significantly without compromising accuracy, aiding new material design.

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Chemistry

Background:

  • Accurate prediction of glass transition temperature (Tg) is crucial for designing new polymeric materials.
  • Molecular dynamics (MD) simulations are a key tool for computational material design.
  • Understanding and quantifying uncertainty in MD predictions is essential.

Purpose of the Study:

  • To apply and evaluate an ensemble approach in MD for predicting Tg and its uncertainty.
  • To develop and validate a novel, time-efficient computational scenario for Tg determination.
  • To analyze the sources of uncertainty in MD-derived Tg values.

Main Methods:

  • Utilized an ensemble approach in molecular dynamics (MD) simulations.
  • Separated uncertainty into aleatoric (dynamical chaos) and computational scenario contributions.
  • Proposed and implemented a concurrent scenario for density-temperature behavior computation.
  • Validated predictions against experimental Tg data obtained via dynamical mechanical analysis.

Main Results:

  • The concurrent MD scenario significantly reduces computation time (days to hours) without increasing aleatoric uncertainty.
  • Both concurrent and sequential MD scenarios showed excellent agreement with experimental Tg for epoxy resins.
  • Confidence intervals for Tg predictions scale as N^-0.5, requiring at least ten ensemble members for 95% confidence within 20 K.

Conclusions:

  • The proposed concurrent MD scenario offers a faster and reliable method for Tg prediction.
  • Ensemble size is critical for achieving desired confidence intervals in MD-based Tg predictions.
  • An optimal MD protocol involves 4 ns burn-in followed by 2 ns production time.