Publication date: Jul 10, 2023
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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|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.|