ZHANG Suncheng; HAN Tongwei; WANG Rumeng; YANG Yantao; ZHANG Xiaoyan

doi:10.7498/aps.74.20241624

Abstract

Tuning graphene’s properties through structural design has received significant attention. However, the complex nonlinear relationship between geometric parameters of structural design and performance needs further exploring to accurately predict the performance of graphene and speed up the optimization of its structural design. 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, it is 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 weak influence. The study further investigates the influence of various perforation geometric parameters on the NPR behavior of graphene. It is found that reducing 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 technologies, this research provides an efficient framework for optimizing the NPR effect in graphene, and theoretical support for its application in smart materials and flexible electronics.

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Authors and contacts

Corresponding author: HAN Tongwei, twhan@ujs.edu.cn

Funds: Project supported by the Major Program of Natural Science Foundation of Higher Education Institutions of Jiangsu Province, China (Grant No. 17KJA130001) and the National Training Program of Innovation and Entrepreneurship for Undergraduates, China (Grant No. 202410299080Z).

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Cited By

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类型 BPNN参数

输入 AR, IS, L

输出 v_xy

隐藏层 91-92-27-60

激活函数 Relu

优化器 Adam

学习率 0.006701783312545704

迭代次数 1000

DownLoad: CSV

评估指标 R² MSE MAE

训练集 0.9990 0.000326 0.01225
测试集 0.9813 0.006755 0.04633

DownLoad: CSV

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Vol. 74, Issue 9 - 2025-05-05

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类型	BPNN参数
输入	AR, IS, L
输出	v_xy
隐藏层	91-92-27-60
激活函数	Relu
优化器	Adam
学习率	0.006701783312545704
迭代次数	1000

评估指标	R²	MSE	MAE
训练集	0.9990	0.000326	0.01225
测试集	0.9813	0.006755	0.04633

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Corresponding author: HAN Tongwei, twhan@ujs.edu.cn

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