Identifying Structural Defects in Disordered Packings of Elongated Particles using Machine Learning

Structural defects within a solid determine where plastic material failure and local rearrangements occur under a driving force. While dislocations and vacancies can be readily identified in a crystalline solid, similar defects can be difficult to find in a disordered solid or packing based solely on structural information. We present an experimental study of a model driven disordered solid--a dry two-dimensional granular pillar under compression--comprised of either circular or elongated particles. A machine learning approach that incorporates a multitude of spherical structure functions can classify circular particles as rearranging or non-rearranging using a linear support vector machine. When constituent particles are asymmetric, spherical structure functions are no longer sufficient; however, our machine learning approach can be modified to yield similar predictive performance for both grain shapes. This method also generates a scalar field, 'softness,' that quantifies how likely each particle is about to undergo a rearrangement. We discuss spatial and temporal characteristics of the rearrangement and softness fields.