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Fragility in glassy liquids: A structural approach based on machine learning.

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Researchers explored why some liquids are strong and others fragile. Using machine learning, they identified "softness" as a key structural factor controlling liquid fragility, offering new insights into glass-forming liquids.

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Area of Science:

  • Condensed matter physics
  • Materials science
  • Computational chemistry

Background:

  • Glassy liquids exhibit a universal rapid increase in viscosity upon cooling.
  • Liquid fragility, describing the deviation from Arrhenius behavior, varies nonuniversally and is poorly understood.
  • Understanding the origins of liquid fragility is crucial for predicting material properties.

Purpose of the Study:

  • To investigate the factors controlling the wide range of fragility observed in glassy liquids.
  • To identify a universal structural descriptor correlated with dynamical rearrangements in supercooled liquids.
  • To elucidate the relationship between structure and dynamics in glass-forming systems.

Main Methods:

  • Simulated a family of harmonic sphere models spanning a wide range of fragility.
  • Employed machine learning (support vector machine) to identify a structural order parameter termed "softness".
  • Analyzed the correlation between softness and dynamical properties like relaxation time and viscosity.

Main Results:

  • Identified "softness," a linear combination of structural quantities, as a universal order parameter across the studied liquids.
  • Demonstrated that softness is highly correlated with dynamical rearrangements and relaxation times.
  • Showcased that softness effectively distinguishes between strong and fragile liquid behaviors.

Conclusions:

  • Softness is a key structural indicator that explains the varying fragility of glass-forming liquids.
  • Machine learning provides a powerful tool for uncovering fundamental structure-dynamics relationships.
  • This work advances the understanding of the microscopic origins of glass transition phenomena.