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Non-equilibrium nature of fracture determines the crack path

Pengjie Shi1, Shizhe Feng1, Zhiping Xu1*

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

* Corresponding authors emails: xuzp@tsinghua.edu.cn
DOI10.24435/materialscloud:af-5v [version v4]

Publication date: Mar 12, 2024

How to cite this record

Pengjie Shi, Shizhe Feng, Zhiping Xu, Non-equilibrium nature of fracture determines the crack path, Materials Cloud Archive 2024.46 (2024), https://doi.org/10.24435/materialscloud:af-5v

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.

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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 on Graphene.
frozen_model_HBN.pb
MD5md5:08ee6f6e03999917c46850ec7dc120d5
27.5 MiB The file is the force field for fracture on HBN

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

Preprint
P. Shi, S. Feng, Z. Xu (in prep)

Keywords

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