Hybrid neural network potential for multilayer graphene
Hybrid neural network potential for multilayer graphene
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DOI：
10.1103/physrevb.100.195419
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发表时间：
2019-09
影响因子：
3.7
通讯作者：
Mingjian Wen;E. Tadmor
中科院分区：
文献类型：
--
作者：
Mingjian Wen;E. Tadmor
单层和多层石墨烯是用于电子设备、传感器、能量产生和存储以及医学等应用的有前途的材料。为了对石墨烯基器件的机械和热行为进行大规模原子模拟，需要精确的原子间势。在这里，我们提出了一种新的多层石墨烯结构原子间势，称为“hNN--Gr$_x$”。这种混合势采用神经网络来描述短程相互作用，并采用理论驱动的分析项来模拟长程色散。该势能针对单层石墨烯、双层石墨烯和基于密度泛函理论 (DFT) 从头算总能量计算获得的石墨构型的大型数据集进行训练。该势为层内和层间相互作用提供准确的能量和力，正确再现结构、能量和弹性特性的 DFT 结果，例如平衡层间距、层间结合能、弹性模量和不适合的声子色散。该电势用于研究空位对单层石墨烯导热性和双层石墨烯层间摩擦的影响。该势可通过位于 \url{this https URL} 的 OpenKIM 原子间势存储库获得。
Monolayer and multilayer graphene are promising materials for applications such as electronic devices, sensors, energy generation and storage, and medicine. In order to perform large-scale atomistic simulations of the mechanical and thermal behavior of graphene-based devices, accurate interatomic potentials are required. Here, we present a new interatomic potential for multilayer graphene structures referred to as "hNN--Gr$_x$." This hybrid potential employs a neural network to describe short-range interactions and a theoretically-motivated analytical term to model long-range dispersion. The potential is trained against a large dataset of monolayer graphene, bilayer graphene, and graphite configurations obtained from ab initio total-energy calculations based on density functional theory (DFT). The potential provides accurate energy and forces for both intralayer and interlayer interactions, correctly reproducing DFT results for structural, energetic, and elastic properties such as the equilibrium layer spacing, interlayer binding energy, elastic moduli, and phonon dispersions to which it was not fit. The potential is used to study the effect of vacancies on thermal conductivity in monolayer graphene and interlayer friction in bilayer graphene. The potential is available through the OpenKIM interatomic potential repository at \url{this https URL}.