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通过结构设计调控石墨烯的性能已引起广泛关注。然而，结构设计几何参数与性能之间存在复杂的非线性关系，如何准确预测石墨烯性能参数加快结构设计仍需进一步深入探索。本文通过引入周期性菱形穿孔缺陷有效地实现了负泊松比石墨烯的结构设计，分析了负泊松比效应的产生机制，并基于反向传播神经网络（BPNN）构建了一种数据驱动的机器学习模型，可实现高效预测并设计具有负泊松比的穿孔石墨烯结构。通过分子动力学模拟构建菱形穿孔石墨烯结构的泊松比数据集，采用优化后的BPNN模型对泊松比进行预测分析，研究发现，穿孔间距比（IS）对菱形穿孔石墨烯结构泊松比的影响最显著，而穿孔纵横比（AR）与晶胞尺寸（L）的影响则相对较弱。本文还研究了不同穿孔几何参数对菱形穿孔石墨烯负泊松比效应的影响规律，减小IS和增大AR能够增强石墨烯结构的负泊松比效应。机器学习模型的预测结果与分子动力学模拟结果高度吻合，验证了机器学习方法在石墨烯泊松比预测中的有效性和可靠性。本研究通过引入菱形穿孔缺陷，结合机器学习技术，实现对石墨烯负泊松比效应的高效预测与优化，为其在智能材料和柔性电子中的应用提供理论支持。Tuning graphene's properties through structural design has garnered significant attention. However, the complex nonlinear relationship between geometric parameters of structural design and performance necessitates further exploration to accurately predict the performance of graphene and accelerate its structural design optimization. This study introduces periodic rhombic perforations to effectively achieve the structural design of graphene with negative Poisson's ratio (NPR). The mechanisms underlying the NPR effect are analyzed, and a data-driven machine learning model based on a backpropagation neural network (BPNN) is developed to efficiently predict and design perforated graphene structures exhibiting NPR. By constructing a Poisson's ratio dataset for rhombic perforated graphene structures through molecular dynamics simulations and employing an optimized BPNN model for predictive analysis, we found that the perforation spacing ratio (IS) has the most significant effect on the Poisson’s ratio of rhombic perforated graphene, while the perforation aspect ratio (AR) and unit cell size (L) have relatively weaker impacts. The study further investigates the impact of various perforation geometric parameters on the NPR behavior of graphene. It was found that decreasing IS and increasing AR can enhance the negative Poisson's ratio effect. The machine learning predictions closely align with molecular dynamics simulation results, demonstrating the effectiveness and reliability of this approach for Poisson's ratio prediction. By integrating rhombic perforation design with machine learning techniques, this research provides an efficient framework for optimizing the NPR effect in graphene, offering theoretical support for its application in smart materials and flexible electronics.
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Keywords:
- graphene /
- negative Poisson's ratio /
- machine learning /
- molecular dynamics
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