Predicting the location of shear band initiation in a metallic glass

TL;DR

This study uses atomistic simulations and deep learning to predict shear band locations in metallic glasses from their initial structure, linking inhomogeneities to mechanical behavior and stability.

Contribution

It introduces a method to predict shear band initiation sites and ductility/brittleness from static structures using deep learning, advancing understanding of inhomogeneities in metallic glasses.

Findings

Shear band locations correlate with initial density of fertile sites.

Initial structure can predict whether a glass is brittle or ductile.

Shear banding arises from non-linear instabilities influenced by inhomogeneities.

Abstract

We report atomistic simulation results which indicate that the location of shear banding in a metallic glass (MG) can be ascertained with reasonably high accuracy solely from the undeformed static structure. Correlation is observed between the location of the initiation of shear bands in a simulated MG and the initial distribution of the density of fertile sites (DFS) for stress-driven shear transformations identified a priori based on a deep learning model devised in our recent work [Fan and Ma, Nat. Commun. 12, 1506 (2021)]. In addition, we demonstrated that one can judge whether a glass is brittle or ductile solely based upon its initial DFS distribution. These validate that shear bands in MG arise from non-linear instabilities, and that the as-quenched glass structure contains inhomogeneities that influence these instabilities. This work also reveals an important subtlety regarding the non-deterministic nature of athermal quasistatic shear simulations.