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Updated: Apr 15, 2026

Measuring Material Microstructure Under Flow Using 1-2 Plane Flow-Small Angle Neutron Scattering
Published on: February 6, 2014
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.
Abstract:
We use machine-learning methods on local structure to identify flow defects-or particles susceptible to rearrangement-in jammed and glassy systems. We apply this method successfully to two very different systems: a two-dimensional experimental realization of a granular pillar under compression and a Lennard-Jones glass in both two and three dimensions above and below its glass transition temperature. We also identify characteristics of flow defects that differentiate them from the rest of the sample. Our results show it is possible to discern subtle structural features responsible for heterogeneous dynamics observed across a broad range of disordered materials.
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