Predicting fracture in disordered network materials using the local intelligent stress threshold indicator
Communications Physics, vol. 8
Abstract
Abstract Network glass fracture occurs as a sequence of elementary events occurring at weak sites in the glass structure. Fracture is a highly complex process that occurs suddenly and without obvious structural or thermodynamic signs prior to the event’s occurrence. We show that a stress threshold value quantified by local mechanical probing highly correlates with nanoscale crack nucleation in a two-dimensional network glass. Subsequently, a neural network-based predictor, the local intelligent stress threshold indicator (LISTI), links the local stress threshold with the undeformed local structural topology. LISTI yields a reliable heatmap indicating soft spots that strongly correlate with the localized initiation and development of the fracture process. Finally, we show that LISTI can be used to find local zones prone to rearrangement in real-measured two-dimensional silica glass structures.
Authors 7
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Affiliation as printed
Institute of General Mechanics, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Institute of General Mechanics, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Institute of General Mechanics, RWTH Aachen University, Aachen, Germany
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Affiliation as printed
Institute of Applied Analysis and Numerical Simulation, University of Stuttgart, Stuttgart, Germany
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Affiliation as printed
Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA
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Affiliation as printed
Department of Materials Science and Engineering, Mechanical Engineering, and Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA
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Affiliation as printed
Institute of General Mechanics, RWTH Aachen University, Aachen, Germany
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