title: Identifying structural flow defects in disordered solids using machine-learning methods.
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.
authors: Department of Physics and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.
authors: E D
authors: ED
authors: Cubuk
authors: Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors: S S
authors: SS
authors: Schoenholz
authors: Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors: J M
authors: JM
authors: Rieser
authors: Department of Physics and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.
authors: B D
authors: BD
authors: Malone
authors: Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada.
authors: J
authors: J
authors: Rottler
authors: Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors: D J
authors: DJ
authors: Durian
authors: Department of Physics and School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.
authors: E
authors: E
authors: Kaxiras
authors: Department of Physics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors: A J
authors: AJ
authors: Liu