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Selecting relevant structural features for glassy dynamics by information imbalance.

Anand Sharma1,2, Chen Liu3, Misaki Ozawa2

  • 1Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, India.

The Journal of Chemical Physics
|November 12, 2024
PubMed
Summary
This summary is machine-generated.

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Creep dynamics of athermal amorphous materials: a mesoscopic approach.

Soft matter·2018
See all related articles

Researchers identified key structural features influencing slow dynamics in glass-forming liquids using an information imbalance technique. This machine learning approach helps uncover mechanisms behind complex glassy behaviors without prior assumptions.

Area of Science:

  • Condensed matter physics
  • Computational materials science

Background:

  • Dynamical heterogeneity is a hallmark of glass-forming liquids, significantly impacting their properties.
  • Identifying structural features that govern slow dynamics is crucial for understanding glass transitions.

Purpose of the Study:

  • To numerically identify key structural features responsible for dynamical heterogeneity in a model glass-forming liquid.
  • To apply a non-parametric machine learning technique for feature selection and dynamics prediction.

Main Methods:

  • Utilized the information imbalance technique for feature selection from physically motivated descriptors.
  • Employed both supervised (dynamical and structural data) and unsupervised (structural data only) learning approaches.
  • Applied selected features to predict future dynamics using machine learning.

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Main Results:

  • Successfully identified relevant structural features contributing to dynamical heterogeneity.
  • Demonstrated the efficacy of the information imbalance technique in a non-parametric, model-agnostic manner.
  • Showcased the capability of selected features to predict future dynamics.

Conclusions:

  • The information imbalance technique is a powerful tool for uncovering structure-dynamics relationships in complex systems.
  • This approach facilitates the identification of dominant mechanisms governing slow dynamics in glassy materials.
  • Potential applications exist in materials design and understanding fundamental properties of disordered systems.