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.

了解断裂对于弹性纳米材料的设计至关重要。分子动力学提供了一种在原子水平上研究断裂的方法，但是由于可扩展性的限制，计算量很大。在这项工作中，我们基于机器学习方法来预测纳米断裂机制，包括裂纹不稳定性和作为晶体取向的函数的分支。我们将重点放在与技术相关的特定材料系统石墨烯上，并将深度学习方法应用于此类纳米材料的研究，并探索将机器学习预测校准为有意义的结果所必需的参数空间。我们的结果验证了深度学习方法能够定量捕获石墨烯断裂行为的能力，包括其分形维数与晶体取向的关系，