Identifying structural flow defects in disordered solids using machine-learning methods

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...

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Veröffentlicht in:	Physical review letters 2015-03, Vol.114 (10), p.108001-108001, Article 108001
Hauptverfasser:	Cubuk, E D, Schoenholz, S S, Rieser, J M, Malone, B D, Rottler, J, Durian, D J, Kaxiras, E, Liu, A J
Format:	Artikel
Sprache:	eng
Schlagworte:	Compressing Defects Dynamics Glass transition temperature Pillars Rest Three dimensional Two dimensional
Online-Zugang:	Volltext
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