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.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Condensed Matter Physics
- Mechanics of Materials
- Mechanical Engineering