Stochastic evolution elasto-plastic modeling of a metallic glass

TL;DR

This paper introduces a data-driven stochastic elastoplastic model for metallic glasses, using atomistic simulation data to capture their complex deformation behavior in a two-dimensional state space.

Contribution

It presents a novel approach that directly leverages simulation data to develop a stochastic elastoplastic model without fitting parameters, specifically tailored for metallic glasses.

Findings

Two state variables suffice to describe deformation physics.

The model reveals rejuvenation and aging balance during steady state.

The approach provides insights into metallic glass response physics.

Abstract

This paper develops a general data-driven approach to stochastic elastoplastic modelling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present inherent complexities due to their disordered structure. By harvesting statistics from simulated metallic glass shear response histories, the material state is mapped onto a two-dimensional state space consisting of the shear stress and the inelastic contribution to the potential energy. The resulting elastoplastic model is intrinsically stochastic and represented as a non-deterministic dynamical map. The state space statistics provide insights into the deformation physics of metallic glasses, revealing that two state variables are sufficient to describe the main features of the elastoplastic response. In this two-dimensional state space, the gradually quenched metallic glass rejuvenates during the initial quasi-elastic shearing, ultimately reaching a steady state that fluctuates about a fixed point in the state space as rejuvenation and aging balance.