Interplay of rearrangements, strain, and local structure during avalanche propagation

TL;DR

This paper investigates how local structure, strain, and particle rearrangements interact during avalanches in jammed soft disks, using machine learning to quantify local environments and informing an improved elastoplastic model.

Contribution

It introduces a machine-learned softness metric to analyze local structure's role in avalanches, enhancing elastoplastic models with structural information.

Findings

Softness correlates strongly with particle rearrangements.

Local structure influences avalanche propagation.

Augmented elastoplastic model incorporates structural data.

Abstract

Jammed soft disks exhibit avalanches of particle rearrangements under quasistatic shear. We follow the avalanches using steepest descent to decompose them into individual localized rearrangements. We characterize the local structural environment of each particle by a machine-learned quantity, softness, designed to be highly correlated with rearrangements, and analyze the interplay between softness, rearrangements and strain. Our findings form the foundation of an augmented elastoplastic model that includes local structure.