Non-Equilibrium Nature of Fracture Determines the Crack Paths

TL;DR

This paper introduces a neural network-based force field that captures the non-equilibrium fracture process in 2D crystals, revealing complex crack patterns and new measures for fracture toughness.

Contribution

It develops NN-F$^{3}$, a high-fidelity neural network force field that models the non-equilibrium fracture behavior and provides novel insights into fracture toughness measures.

Findings

Fracture patterns are influenced by intermediate, unrelaxed states.

Fracture resistance is better quantified by unrelaxed edge energy densities.

Crack path complexity ranges from lattice kinks to large-scale patterns.

Abstract

A high-fidelity neural network-based force field, NN-F$^{3}$, is developed to cover the strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F$^{3}$ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be quantified by the energy densities of relaxed edges as in the literature. Instead, the fracture patterns, critical stress intensity factors at the kinks, and energy densities of edges in the intermediate, unrelaxed states offer reasonable measures for the fracture toughness and its anisotropy.