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Identifying structural flow defects in disordered solids using machine-learning methods
E D Cubuk1, S S Schoenholz2, J M Rieser2
1Department of Physics and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.
Physical Review Letters
|March 28, 2015
Summary
Machine learning identifies flow defects, or particles prone to rearrangement, in jammed and glassy systems. This method reveals structural features linked to heterogeneous dynamics in disordered materials.
Area of Science:
- Physics
- Materials Science
- Computational Science
Background:
- Jammed and glassy systems exhibit complex behaviors due to particle rearrangements.
- Understanding these rearrangements is crucial for predicting material properties.
- Heterogeneous dynamics are a hallmark of disordered materials.
Purpose of the Study:
- To develop a machine-learning method for identifying flow defects in jammed and glassy systems.
- To apply this method to diverse physical systems.
- To characterize the structural features of flow defects.
Main Methods:
- Utilized machine-learning algorithms focused on local structural properties.
- Applied the method to a 2D granular pillar under compression.
- Analyzed Lennard-Jones glasses in 2D and 3D, across the glass transition temperature.
Main Results:
- Successfully identified flow defects in both granular and Lennard-Jones glass systems.
- Distinguished characteristics of flow defects from the bulk material.
- Demonstrated the method's applicability to different dimensions and states.
Conclusions:
- Machine learning effectively identifies critical structural features related to flow defects.
- These findings provide insight into the origins of heterogeneous dynamics in disordered matter.
- The approach is broadly applicable to various disordered materials.
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