Predicting the location of shear band initiation in a metallic glass

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.

arXiv comment16 pages, 5 figures

References in corpus (17)

• Dynamics of Viscoplastic Deformation in Amorphous Solids
• Identifying structural flow defects in disordered solids using machine learning methods
• Strain localization and percolation of stable structure in amorphous solids
• A random critical point separates brittle and ductile yielding transitions in amorphous materials
• Universal Breakdown of Elasticity at the Onset of Material Failure
• Predicting plasticity in disordered solids from structural indicators
• Strain localization in a shear transformation zone model for amorphous solids
• Evaluation of the "disorder temperature" and "free volume" formalisms via simulations of shear banding in amorphous solids
• Quantitative measurement of density in a shear band of metallic glass monitored along its propagation direction
• A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
• Rate dependent shear bands in a shear transformation zone model of amorphous solids
• Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
• Size-independent Shear Band Formation in Amorphous Nanowires made from Simulated Casting
• Rejuvenation and Shear-Banding in model amorphous solids
• Anisotropic Structural Predictor in Glassy Materials
• Shear Band Broadening in Simulated Glasses
• Flow heterogeneities in supercooled liquids and glasses under shear

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