Pengjie Shi¹, Shizhe Feng¹, Zhiping Xu^1*

1 Applied Mechanics Laboratory and Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China

* Corresponding authors emails: xuzp@tsinghua.edu.cn

DOI10.24435/materialscloud:rd-0e [version v3]

Publication date: Jul 10, 2023

How to cite this record

Pengjie Shi, Shizhe Feng, Zhiping Xu, Non-equilibrium nature of fracture determines the crack path, Materials Cloud Archive 2023.108 (2023), https://doi.org/10.24435/materialscloud:rd-0e

Description

A high-fidelity neural network-based force field (NN-F³) is developed to cover the space of strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F³ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be captured by the energy densities of relaxed edges as used 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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:bc744e9f8ada09e6ec67000ed6f6e4f9	1.1 KiB	README file detailing the contents of this record.
NNF3-data-set.zip MD5md5:1694517116a191d22113df8c55512358	489.0 MiB	This file contains the dataset to train the neural-network force field for fracture.
frozen_model.pb MD5md5:a6e2b4ae8881179b338b16a791087977	3.9 MiB	The file is the force field for fracture.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

graphene 2D materials machine learning neural-newrok force field fracture anisotropy fracture mechanics