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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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[1] Lee C, Wei X D, Kysar J W and Hone J 2008 Science 321 385
[2] Zhang Y B, Tan Y W, Stormer H L and Kim P 2005 Nature 438 201
[3] Novoselov K S, Geim A K, Morozov S V, Jiang D, Zhang Y, Dubonos S V, Grigorieva I V and Firsov A A 2004 Science 306 666
[4] Balandin A A, Ghosh S, Bao W Z, Calizo I, Teweldebrhan D, Miao F and Lau C N 2008 Nano Lett. 8 902
[5] Jang H, Park Y J, Chen X, Das T, Kim M S and Ahn J H 2016 Adv. Mater. 28 4184
[6] Huang C and Chen L 2016 Adv. Mater. 28 8079
[7] Grima J N, Winczewski S, Mizzi L, Grech M C, Cauchi R, Gatt R, Attard D, Wojciechowski K W and Rybicki J 2015 Adv. Mater. 27 1455
[8] Zhai Z R, Wu L L and Jiang H Q 2021 Appl. Phys. Rev. 8 041319
[9] Yu Y, Yin Y Q, Bai R Y, Hu Y Z, Li B, Wang M Y and Chen G M 2023 Appl. Phys. Lett. 123 011702
[10] Sun R J, Zhang B, Yang L, Zhang W J, Farrow I, Scarpa F and Rossiter J 2018 Appl. Phys. Lett. 112 251904
[11] Han D X, Chen S H, Zhao L, Tong X and Chan K C 2022 AIP Adv. 12 035305
[12] Lee J H, Singer J P and Thomas E L 2012 Adv. Mater. 24 4782
[13] Grima J N, Manicaro E and Attard D 2011 Proc. R. Soc. A: Math. Phys. Eng. Sci. 467 439
[14] Han T W, Scarpa F and Allan N L 2017 Thin Solid Films 632 35
[15] Ho V H, Ho D T, Kwon S Y and Kim S Y 2016 Phys. Status Solidi B Basic Res. 253 1303
[16] Cai K, Luo J, Ling Y R, Wan J and Qin Q H 2016 Sci. Rep. 6 35157
[17] Shi P, Chen Y, Feng J and Sareh P 2024 Phys. Rev. E 109 035002
[18] Shi P, Chen Y, Wei Y, Feng J, Guo T, Tu Y M and Sareh P 2023 Phys. Rev. B 108 134105
[19] Wan J, Jiang J W and Park H S 2017 Nanoscale 9 4007
[20] Jiang J W, Chang T C and Guo X M 2016 Nanoscale 8 15948
[21] Hanakata P Z, Cubuk E D, Campbell D K and Park H S 2018 Physi. Rev. Lett. 121 255304
[22] Wan J, Jiang J W and Park H S 2020 Carbon 157 262
[23] Rumelhart D E, Hinton G E and Williams R J 1986 Nature 323 533
[24] Slann A, White W, Scarpa F, Boba K and Farrow I 2015 Phys. Status Solidi B Basic Res. 252 1533
[25] Grima J N, Mizzi L, Azzopardi K M and Gatt R 2016 Adv. Mater. 28 385
[26] Thompson A P, Aktulga H M, Berger R, Bolintineanu D S, Brown W M, Crozier P S, Veld P J I, Kohlmeyer A, Moore S G, Nguyen T D, Shan R, Stevens M J, Tranchida J, Trott C and Plimpton S J 2022 Comput. Phys. Comm. 271 10817
[27] Dhaliwal G, Nair P B and Singh C V 2019 Carbon 142 300
[28] Stuart S J, Tutein A B and Harrison J A 2000 J. Chem. Phys. 112 6472
[29] Qin H S, Sun Y, Liu J Z and Liu Y L 2016 Carbon 108 204
[30] Qian C, McLean B, Hedman D and Ding F 2021 APL Mater. 9 061102
[31] Swope W C, Andersen H C, Berens P H and Wilson K R 1982 J. Chem. Phys. 76 637
[32] Hoover W G 1985 Phys. Rev. A Gen. Phys. 31 1695
[33] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M and Duchesnay É 2011 J. Mach. Learn. Res. 11 2825
[34] Jones D R, Schonlau M and Welch W J 1998 J. Glob. Optim. 13 455
[35] Lundberg S M and Lee S-I 2017 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 2017 pp 4768 - 4777
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