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Exploring glassy dynamics with Markov state models from graph dynamical neural networks.

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Machine learning reveals slow dynamics in glass formers using a Markov state model (MSM). This approach identifies structural heterogeneities and local packing fluctuations, crucial for understanding the glass transition.

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Area of Science:

  • Computational physics
  • Materials science
  • Machine learning

Background:

  • Understanding the glass transition is a long-standing challenge in condensed matter physics.
  • Molecular dynamics simulations are essential for studying the dynamics of glass-forming materials.
  • Identifying slow dynamics and structural heterogeneities is key to explaining glass behavior.

Purpose of the Study:

  • To develop a machine learning approach for analyzing molecular dynamics simulations of glass formers.
  • To reveal structural heterogeneities and their associated slow dynamics.
  • To connect simulation findings with established theories of the glass transition.

Main Methods:

  • Utilized machine learning to construct a Markov state model (MSM).
  • Coarse-grained molecular dynamics into a low-dimensional feature space.
  • Analyzed transition timescales and mapped states to local excess Voronoi volume.

Main Results:

  • The developed MSM effectively captures slow dynamics in a model glass former.
  • Identified structural heterogeneities corresponding to local excess Voronoi volume.
  • Obtained transition timescales larger than structural relaxation time from shorter trajectories.

Conclusions:

  • Local packing fluctuations are identified as dominant slowly relaxing features in glass formers.
  • The findings support classic free volume theories of the glass transition.
  • MSM provides an efficient method to study slow dynamics in complex systems.