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Updated: Mar 22, 2026

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films
Published on: January 26, 2016
Structural Properties of Defects in Glassy Liquids
Ekin D Cubuk1, Samuel S Schoenholz1,2, Efthimios Kaxiras1
1Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University , Cambridge, Massachusetts 02138, United States.
Softness, a machine-learning identified particle property, simplifies understanding glassy dynamics by predicting particle rearrangements. This structural quantity offers superior predictive power compared to traditional measures like coordination number.
Area of Science:
- Condensed Matter Physics
- Materials Science
- Computational Materials Science
Background:
- Disordered solids exist in local energy minima.
- Temperature or mechanical load induces particle rearrangements, exploring these minima.
- Understanding these dynamics is crucial for materials behavior.
Purpose of the Study:
- Investigate the correlation between particle softness and other structural quantities.
- Evaluate softness's predictive power for rearrangement dynamics.
- Introduce a metric to quantify the predictive quality of structural measures.
Main Methods:
- Supervised machine learning to identify particle softness.
- Correlation analysis with coordination number and local potential energy.
- Unsupervised dimensionality reduction and curve-fitting models.
Main Results:
- Softness strongly correlates with coordination number and local potential energy.
- Softness demonstrates significantly higher predictive power for rearrangement dynamics.
- A new metric for evaluating predictors of dynamics is introduced.
- Softness can be analytically treated.
Conclusions:
- Softness is a superior predictor of particle rearrangement dynamics in disordered solids.
- The introduced metric facilitates quantitative comparison of structural measures.
- Further exploration of softness's physical meaning and analytical treatment is warranted.
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