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

17 引文 斯高帕斯(Scopus)


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.

期刊npj 2D Materials and Applications
出版狀態Published - 2021 12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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