Deep learning model to predict fracture mechanisms of graphene

Andrew J. Lew, Chi Hua Yu, Yu Chuan Hsu, Markus J. Buehler

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number48
Journalnpj 2D Materials and Applications
Volume5
Issue number1
DOIs
Publication statusPublished - 2021 Dec

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Deep learning model to predict fracture mechanisms of graphene'. Together they form a unique fingerprint.

Cite this