Abstract Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction. A part of the data required to model the shallow networks was obtained by developing an analytical solution based on the Bailey durability criterion and the Arrhenius equation. Molecular dynamics (MD) simulations were also used to obtain some data. Sensitivity analysis was performed to explore the features learnt by the neural network, and their behaviour under extrapolation was also investigated. Subsequently, deep convolutional neural networks (CNNs) were developed to predict the fracture stress of graphene samples containing random distributions of vacancy defects. Data required to model CNNs was obtained from MD simulations. Our results reveal that the neural networks have a strong ability to predict the fracture stress of defective graphene under various processing conditions. In addition, this work highlights some advantages as well as limitations and challenges in using neural networks to solve complex problems in the domain of computational materials design.

中文翻译：

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使用浅层和深层人工神经网络表征缺陷石墨烯样品的断裂应力

摘要 先进的机器学习方法可能有助于获得对一些具有挑战性的纳米力学问题的新见解。在这项工作中，我们采用人工神经网络来预测有缺陷的石墨烯样品的断裂应力。首先，使用浅层神经网络来预测断裂应力，断裂应力取决于温度、空位浓度、应变率和加载方向。建立浅层网络模型所需的部分数据是通过基于贝利耐久性准则和阿伦尼乌斯方程开发解析解获得的。分子动力学 (MD) 模拟也用于获得一些数据。进行敏感性分析以探索神经网络学习的特征，并且还研究了它们在外推下的行为。随后，开发了深度卷积神经网络 (CNN) 来预测包含空位缺陷随机分布的石墨烯样品的断裂应力。建模 CNN 所需的数据是从 MD 模拟中获得的。我们的结果表明，神经网络具有很强的预测缺陷石墨烯在各种加工条件下的断裂应力的能力。此外，这项工作突出了使用神经网络解决计算材料设计领域中的复杂问题的一些优势以及局限性和挑战。我们的结果表明，神经网络具有很强的预测缺陷石墨烯在各种加工条件下的断裂应力的能力。此外，这项工作突出了使用神经网络解决计算材料设计领域中的复杂问题的一些优势以及局限性和挑战。我们的结果表明，神经网络具有很强的预测缺陷石墨烯在各种加工条件下的断裂应力的能力。此外，这项工作突出了使用神经网络解决计算材料设计领域中的复杂问题的一些优势以及局限性和挑战。