Deep learning model to predict fracture mechanisms of graphene

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

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
文章編號48
期刊npj 2D Materials and Applications
5
發行號1
DOIs
出版狀態Published - 2021 十二月

All Science Journal Classification (ASJC) codes

  • 化學 (全部)
  • 材料科學(全部)
  • 凝聚態物理學
  • 材料力學
  • 機械工業

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