<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Shi, Pengjie</dc:creator> <dc:creator>Feng, Shizhe</dc:creator> <dc:creator>Xu, Zhiping</dc:creator> <dc:date>2023-07-10</dc:date> <dc: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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.108</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:rd-0e</dc:identifier> <dc:identifier>mcid:2023.108</dc:identifier> <dc:identifier>oai:materialscloud.org:1822</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>graphene</dc:subject> <dc:subject>2D materials</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>neural-newrok force field</dc:subject> <dc:subject>fracture anisotropy</dc:subject> <dc:subject>fracture mechanics</dc:subject> <dc:title>Non-equilibrium nature of fracture determines the crack path</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>