Year: 2021
Type: article
Abstract: Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,990
Funders: National Science Foundation, National Institutes of Health, Multidisciplinary University Research Initiative, Quest for Intelligence, Massachusetts Institute of Technology, Office of Naval Research, National Institute of Biomedical Imaging and Bioengineering, Air Force Office of Scientific Research, Army Research Office
OpenAlex ID: W3165803053