Microstructural features governing fracture of a two-dimensional amorphous solid identified by machine learning

TL;DR

This study combines microscopy, particle tracking, and machine learning to analyze microstructural features influencing crack propagation in a 2D amorphous colloidal solid, revealing local density as a key factor.

Contribution

It introduces a novel experimental and computational approach to predict fracture regions in amorphous materials using machine learning.

Findings

Local density is more influential than orientational order in crack formation.

Machine learning effectively predicts regions likely to fracture.

Microstructural analysis enhances understanding of fracture mechanisms.

Abstract

Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, using a quantified representation of the microstructure in combination with a machine learning algorithm, we calculate the likelihood of regions of the monolayer to be on a crack line, which we term Weakness. From this analysis, we identify the most important contributions to crack propagation and find that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process.