Structuro-elasto-plasticity (StEP) model for plasticity in disordered solids

TL;DR

This paper introduces a new model called StEP that uses machine learning-derived softness to better predict plasticity in disordered solids, capturing the interplay between structure and plastic events.

Contribution

The paper develops a structuro-elasto-plasticity model incorporating softness, a machine learning-based structural quantity, to improve predictions of plasticity in disordered solids.

Findings

The StEP model quantitatively matches particle simulation results.

Softness correlates with local yield strain and plastic rearrangements.

The model captures the interplay between structure, plasticity, and elasticity.

Abstract

Elastoplastic lattice models for the response of solids to deformation typically incorporate structure only implicitly via a local yield strain that is assigned to each site. However, the local yield strain can change in response to a nearby or even distant plastic event in the system. This interplay is key to understanding phenomena such as avalanches in which one plastic event can trigger another, leading to a cascade of events, but typically is neglected in elastoplastic models. To include the interplay one could calculate the local yield strain for a given particulate system and follow its evolution, but this is expensive and requires knowledge of particle interactions, which is often hard to extract from experiments. Instead, we introduce a structural quantity, "softness," obtained using machine learning to correlate with imminent plastic rearrangements. We show that softness also correlates with local yield strain. We incorporate softness to construct a "structuro-elasto-plasticity" model that reproduces particle simulation results quantitatively for several observable quantities, confirming that we capture the influence of the interplay of local structure, plasticity, and elasticity on material response.