Deep learning model to predict fracture mechanisms of graphene

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.
NSF (Grant 1122374)
Office of Naval Research (Grants N000141612333 and N000141912375)
AFOSR-MURI (Contract FA9550-15-1-0514)
Army Research Office (Contract W911NF1920098)
NIH (Grant U01-EB014976)