Learning local, quenched disorder in plasticity and other crackling noise phenomena

TL;DR

This paper introduces a formalism to extract local quenched disorder distributions from field-response data in crackling noise models, enhancing understanding of plasticity phenomena.

Contribution

A novel method for deriving local disorder distributions from time series data in models of crackling noise and plasticity.

Findings

Method effectively recovers disorder distributions in models.

Higher temporal resolution improves accuracy.

Applicable to models of crystalline and amorphous plasticity.

Abstract

When far from equilibrium, many-body systems display behavior that strongly depends on the initial conditions. A characteristic such example is the phenomenon of plasticity of crystalline and amorphous materials that strongly depends on the material history. In plasticity modeling, the history is captured by a quenched, local and disordered flow stress distribution. While it is this disorder that causes avalanches that are commonly observed during nanoscale plastic deformation, the functional form and scaling properties have remained elusive. In this paper, a generic formalism is developed for deriving local disorder distributions from field-response (e.g. stress/strain) timeseries in models of crackling noise. We demonstrate the efficiency of the method in the hysteretic random-field Ising model and also, models of elastic interface depinning that have been used to model crystalline and amorphous plasticity. We show that the capacity to resolve the quenched disorder distribution improves with the temporal resolution and number of samples.