Graphene is one of the most researched two dimensional (2D) material due to its unique combination of mechanical, thermal and electrical properties. Special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young's modulus, high specific strength etc. which are critical for myriad of applications including light weight structural materials, multi-functional coating and flexible electronics. It is quite challenging and costly to experimentally investigate graphene/graphene based nanocomposites, computational simulations such as molecular dynamics (MD) simulations are widely adopted for understanding the microscopic origins of their unique properties. However, disparate results were reported from computational studies, especially MD simulations using various empirical inter-atomic potentials. In this work, an artificial neural network based interatomic potential has been developed for graphene to represent the potential energy surface based on first principle calculations. The developed machine learning potential (MLP) facilitates high fidelity MD simulations to approach the accuracy of ab initio methods but with a fraction of computational cost, which allows larger simulation size/length, and thereby enables accelerated discovery/design of graphene-based novel materials. Lattice parameter, coefficient of thermal expansion (CTE), Young's modulus and yield strength are estimated using machine learning accelerated MD simulations (MLMD), which are compared to experimental/first principle calculations from previous literatures. It is demonstrated that MLMD can capture the dominating mechanism governing CTE of graphene, including effects from lattice parameter and out of plane rippling.

翻译：石墨烯因其独特的力学、热学和电学性能组合，成为研究最广泛的二维材料之一。石墨烯特殊的二维结构使其展现出高杨氏模量、高比强度等一系列独特材料特性，这些特性对于轻质结构材料、多功能涂层和柔性电子等众多应用至关重要。由于实验研究石墨烯/石墨烯基纳米复合材料难度大且成本高，分子动力学模拟等计算模拟方法被广泛用于理解其独特性质的微观起源。然而，计算研究（尤其是采用各种经验原子间势的分子动力学模拟）报告的结果存在显著差异。本研究基于第一性原理计算，开发了一种基于人工神经网络的石墨烯原子间势，以表征其势能面。所开发的机器学习势能以极低的计算成本实现接近从头算方法精度的分子动力学模拟，从而支持更大规模/更长时间的模拟，加速石墨烯基新型材料的发现与设计。采用机器学习加速分子动力学模拟估算了晶格常数、热膨胀系数、杨氏模量和屈服强度，并与文献中的实验/第一性原理计算结果进行了比较。结果表明，机器学习加速分子动力学模拟能够捕捉控制石墨烯热膨胀系数的主导机制，包括晶格参数和面外褶皱效应。