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Crack path selection in 2D crystals

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:k4-nj [version v1]

Publication date: Mar 24, 2023

How to cite this record

Pengjie Shi, Shizhe Feng, Zhiping Xu, Crack path selection in 2D crystals, Materials Cloud Archive 2023.49 (2023), doi: 10.24435/materialscloud:k4-nj.


Even though fracture in solids can be rationalized in continuum mechanics, local processes such as the path selection and the formation of facets and kinks along the crack edges cannot be resolved in this framework. The problem is addressed by developing a high-fidelity neural network-based force field NN-F3. An active-learning approach is taken to capture the high strain and non-equilibrium nature of the crack tips in 2D crystals such as graphene and h-BN, which has not been satisfactorily addressed by existing empirical and machine-learning force fields. Atomistic simulations using NN-F3 resolve spatial complexities from lattice-scale kinks to sample-scale crack patterns, which are discussed directly with continuum mechanics predictions and experimental observation. The results show that kinking or deflection of the cracks defines the roughness of cleaved edges and is explained by the alternation of the stress intensity factors. We also find that the selection of crack paths cannot be determined by the anisotropy in the energies of relaxed edges as widely referred to in the literature. The distortion and undercoordination of the cleaved edges play critical roles in the fracture process, which have to be incorporated into the models to predict the crack paths. Measures of fracture toughness are extracted from the fracture patterns, the critical stress intensity factors, or the energies of edges in the intermediate, unrelaxed states.

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File name Size Description
1.1 KiB README file detailing the contents of this record.
489.0 MiB This file contains the dataset to train the neural-network force field for fracture.
3.9 MiB The file is the force field for fracture.


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Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

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


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

Version history:

2023.49 (version v1) [This version] Mar 24, 2023 DOI10.24435/materialscloud:k4-nj