EDP Sciences logo
Submit a paper
Search
Menu
All issues Volume 12 (2025) EPJ Appl. Metamat., 12 (2025) 7 References
Open Access
Issue
EPJ Appl. Metamat.
Volume 12, 2025
Article Number 7
Number of page(s) 15
DOI https://doi.org/10.1051/epjam/2025006
Published online 22 December 2025
  1. X. Zhao, G. Duan, A. Li, C. Chen, X. Zhang, Integrating microsystems with metamaterials towards metadevices, Microsyst. Nanoeng. 5, 5 (2019) [Google Scholar]
  2. Z.L. Wang, X. Wang, J.L. Wang, Research advance on the sensing characteristics of refractive index sensors based on electromagnetic metamaterials, Adv. Condens. Matter Phys. 2021, 27 (2021) [Google Scholar]
  3. B. Wood, Structure and properties of electromagnetic metamaterials, Laser Photonics Rev. 1, 249 (2007) [Google Scholar]
  4. X.J. Liu, F. Xia, M. Wang, J. Liang, M.J. Yun, Working mechanism and progress of electromagnetic metamaterial perfect absorber, Photonics 10, 205 (2023) [Google Scholar]
  5. H. Ammari, W. Wu, S. Yu, Double-negative electromagnetic metamaterials due to chirality, Quart. Appl. Math. 77, 105 (2018) [Google Scholar]
  6. M.R.C. Mahdy, A. Al Sayem, A. Shahriar, J. Shawon, G.D. Al-Quaderi, I. Jahangir, M.A. Matin, Electromagnetic metamaterial-inspired band gap and perfect transmission in semiconductor and graphene-based electronic and photonic structures, Eur. Phys. J. Plus 131, 92 (2016) [Google Scholar]
  7. Y.S. Feng, M.S. Liang, X.G. Zhao, R. You, Fabrication and modulation of flexible electromagnetic metamaterials, Microsyst. Nanoeng. 11, 14 (2025) [Google Scholar]
  8. F. Yenilmez, I. Mutlu, Production of metamaterial-based radar absorbing material for stealth technology, Braz. J. Phys. 54, 60 (2024) [Google Scholar]
  9. J.P. Peng, S.M. Wang, B.Y. Liang, Q.Y. Wen, C.L. Sun, K. Li, X.S. Zhang, Y. Zhang, Review of micro and nano scale 3D printing of electromagnetic metamaterial absorbers: mechanism, fabrication, and functionality, Virtual Phys. Protot. 19, 39 (2024) [Google Scholar]
  10. D.H. Han, W.K. Li, M. Liu, T. Sun, Y.S. Hou, X.M. Chen, H.Y. Shi, Z.J. Fan, X.Q. Wang, F.Q. Meng, L.Y. Zhang, X.F. Chen, Kirigami-inspired planar deformable metamaterials for multiple dynamic electromagnetic manipulations, Laser Photonics Rev. 17, 2300374 (2023) [Google Scholar]
  11. T.R. Howarth, Hot topics in engineering acoustics: acoustic metamaterials, J. Acoust. Soc. Am. 128, 2390 (2010) [Google Scholar]
  12. G.X. Li, X.A. Luo, T.J. Cui, Optical metamaterials and information metamaterials, Adv. Opt. Mater. 12, 2303101 (2024) [Google Scholar]
  13. H.L. An, J. Yuan, J. Li, L.Q. Cao, Long-distance and anti-disturbance wireless power transfer based on concentric three-coil resonator and inhomogeneous electromagnetic metamaterials, Int. J. Circuit Theory Appl. 51, 2030 (2023) [Google Scholar]
  14. J.L. Wang, Z.X. Yang, X.C. Dong, L. Yin, H.D. Wan, H.M. Chen, K. Zhong, VO2 based terahertz anisotropic coding metasurface, Acta Physica Sinica 72, 124204 (2023) [Google Scholar]
  15. Y.X. Wang, Y. Li, Z.C. Tang, H. Li, Z.L. Yuan, H.G. Tao, N.L. Zou, T. Bao, X.H. Liang, Z.Z. Chen, S.H. Xu, C. Bian, Z.M. Xu, C. Wang, C. Si, W.H. Duan, Y. Xu, Universal materials model of deep-learning density functional theory Hamiltonian, Sci. Bull. 69, 2514 (2024) [Google Scholar]
  16. X. Jiang, H.D. Fu, Y. Bai, L. Jiang, H.T. Zhang, W.R. Wang, P.W. Yun, J.J. He, D.Z. Xue, T. Lookman, Y.J. Su, J.X. Xie, Interpretable machine learning applications: a promising prospect of AI for materials, Adv. Funct. Mater. 35, 2507734 (2025) [Google Scholar]
  17. A.M. Mroz, A.R. Basford, F. Hastedt, I.S. Jayasekera, I. Mosquera-Lois, R. Sedgwick, P.J. Ballester, J.D. Bocarsly, E.A.D. Chanona, M.L. Evans, J.M. Frost, A.M. Ganose, R.L. Greenaway, K.K. Hii, Y.Z. Li, R. Misener, A. Walsh, D.D. Zhang, K.E. Jelfs, Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry, Chem. Soc. Rev. 54, 5433 (2025) [Google Scholar]
  18. G. Yang, Q. Xiao, Z. Zhang, Z. Yu, X. Wang, Q. Lu, Exploring AI in metasurface structures with forward and inverse design, iScience 28, 111995 (2025) [Google Scholar]
  19. Y. Shan, C. Wang, B. Wen, W. Tang, J. Wang, H. Chen, K. Jiang, X. Zhang, W. Xia, Perspective on low-dimensional carbon materials: synergistic advancement of electromagnetic wave attenuation and AI-driven functionalization, Carbon 245, 120783 (2025) [Google Scholar]
  20. Y. Hu, J. Huang, J. Li, Y. Sui, J. Zhang, T. Zheng, W. Wang, C. Zhu, Q. Guo, L. Li, X. Zhang, N. Chen, J. Chen, Y. Chen, X. Zheng, Z. Yang, J. Chen, Artificial intelligence in nanophotonics: from design to optical computing, Chin. Phys. Lett. 42, 8 (2025) [Google Scholar]
  21. C. Qian, I. Kaminer, H.S. Chen, A guidance to intelligent metamaterials and metamaterials intelligence, Nat. Commun. 16, 1154 (2025) [Google Scholar]
  22. W.P. Huang, W. Tang, Z.W. Chen, L.H. Tang, C. Chen, L.F. Hou, Reinforcement-learning empowered adaptive piezoelectric metamaterial for variable-frequency vibration attenuation, Eng. Struct. 332, 120013 (2025) [Google Scholar]
  23. J. Song, J. Lee, N. Kim, K. Min, Artificial intelligence in the design of innovative metamaterials: a comprehensive review, Int. J. Precis. Eng. Manuf. 25, 225 (2024) [Google Scholar]
  24. X.Y. Ma, Z.R. Wang, W.P. Zhang, P. Yan, Programmable metamaterials with perforated shell group supporting versatile information processing, Adv. Sci. 12, 2417784 (2025) [Google Scholar]
  25. K.K. Chen, X.J. Dong, P.L. Gao, Q. Chen, Z.K. Peng, G. Meng, Physics-informed neural networks for topological metamaterial design and mechanical applications, Int. J. Mech. Sci. 301, 110489 (2025) [Google Scholar]
  26. L.L. Li, T.J. Cui, Information metamaterials − from effective media to real-time information processing systems, Nanophotonics 8, 703 (2019) [Google Scholar]
  27. P.C. Jiao, Mechanical energy metamaterials in interstellar travel, Prog. Mater. Sci. 137, 101132 (2023) [Google Scholar]
  28. W.J. Luo, S. Yan, J. Zhou, Ceramic-based dielectric metamaterials, Interdiscip. Mater. 1, 11 (2022) [Google Scholar]
  29. T. Tan, Z.M. Yan, H.X. Zou, K.J. Ma, F.R. Liu, L.C. Zhao, Z.K. Peng, W.M. Zhang, Renewable energy harvesting and absorbing via multi-scale metamaterial systems for Internet of things, Appl. Energy 254, 113717 (2019) [Google Scholar]
  30. P.K. Sharma, D. Sharma, T.J.V.S. Rao, J.R. Szymański, M. Żurek-Mortka, M. Sathiyanarayanan, Design & implementation of a meta-material based ultra-wide band microstrip patch antenna for vehicular communication, Microsyst. Technol. 31, 547 (2024) [Google Scholar]
  31. H.Z. Guo, Z. Sun, C. Zhou, Practical design and implementation of metamaterial-enhanced magnetic induction communication, IEEE Access 5, 17213 (2017) [Google Scholar]
  32. S. Abadal, C. Liaskos, A. Tsioliaridou, S. Ioannidis, A. Pitsillides, J. Solé-Pareta, E. Alarcón, A. Cabellos-Aparicio, Computing and communications for the software-defined metamaterial paradigm a context analysis, IEEE Access 5, 6225 (2017) [Google Scholar]
  33. F.Y. Dong, L.D. Shao, C.N. Niu, W.R. Zhu, Optically transparent microwave metamaterial absorbers, J. Optics 27, 043004 (2025) [Google Scholar]
  34. J.Y. Zhang, B. Hu, S.B. Wang, Review and perspective on acoustic metamaterials: from fundamentals to applications, Appl. Phys. Lett. 123, 010502 (2023) [Google Scholar]
  35. A.S. Dhillon, D. Mittal, R. Bargota, Triple band ultrathin polarization insensitive metamaterial absorber for defense, explosive detection and airborne radar applications, Microw. Opt. Technol. Lett. 61, 89 (2018) [Google Scholar]
  36. Y. Luo, K.P. Qiu, T.Q. Xu, F.L. Zhang, P. Yue, Design of a novel multiband terahertz metamaterial absorber using a deep learning framework, Eng. Appl. Artif. Intell. 151, 110801 (2025) [Google Scholar]
  37. S. Yin, H.T. Zhong, W. Huang, W.T. Zhang, Deep learning enabled design of terahertz high-Q metamaterials, Opt. Laser Technol. 181, 111684 (2025) [Google Scholar]
  38. S. Xie, L. Gu, J. Guo, Design of chiral plasmonic metamaterials based on interpretable deep learning, J. Phys. D: Appl. Phys. 57, 045103 (2023) [Google Scholar]
  39. L. Zhu, W. Hua, C. Lv, Y. Liu, Rapid inverse design of high degree of freedom meta-atoms based on the image-parameter diffusion model, J. Lightwave Technol. 42, 5269 (2024) [Google Scholar]
  40. X.Y. Zheng, J. Shiomi, T. Yamada, Optimizing metamaterial inverse design with 3D conditional diffusion model and data augmentation, Adv. Mater. Technol. 10, 2500293 (2025) [Google Scholar]
  41. X. Lyu, X. Ren, Variational autoencoder guided conditional diffusion generative model for material microstructure reconstruction and inverse design, Mater. Today Commun. 48, 113087 (2025) [Google Scholar]
  42. Z. Liu, D. Zhu, S.P. Rodrigues, K.-T. Lee, Cai, Wenshan, A generative model for inverse design of metamaterials. arXiv:1805.10181 [physics.optics] (2018) [Google Scholar]
  43. A.J.D. Shaikeea, Exploration of truss metamaterials with graph based generative modeling, Nat. Commun. 14, 75651 (2023) [Google Scholar]
  44. H.Y. Zhang, J. Paik, Kirigami design and modeling for strong, lightweight metamaterials, Adv. Funct. Mater. 32, 2107401 (2022) [Google Scholar]
  45. J. Sim, S. Wu, S. Hwang, L. Lu, R.R. Zhao, Selective actuation enabled multifunctional magneto-mechanical metamaterial for programming elastic wave propagation, Adv. Funct. Mater. 35, 2422325 (2025) [Google Scholar]
  46. A. Li, X. Zhao, G. Duan, S. Anderson, X. Zhang, Metamaterials: diatom frustule‐inspired metamaterial absorbers: the effect of hierarchical pattern arrays, Adv. Funct. Mater. 29, 1809029 (2019) [Google Scholar]
  47. C. Xiao, M. Liu, K. Yao, Y. Zhang, M. Zhang, M. Yan, Y. Sun, X. Liu, X. Cui, T. Fan, C. Zhao, W. Hua, Y. Ying, Y. Zheng, D. Zhang, C.-W. Qiu, H. Zhou, Ultrabroadband and band-selective thermal meta-emitters by machine learning, Nature 643, 80 (2025) [Google Scholar]
  48. J.T. Orasugh, A. Mohanty, A. Malakar, S. Bose, S.S. Ray, Design and applications of multi-frequency programmable metamaterials for adaptive stealth, Adv. Funct. Mater. 12, e13726 (2025) [Google Scholar]
  49. L.M. Si, R. Niu, C.Y. Dang, X.E. Bao, Y.Q. Zhuang, W.R. Zhu, Advances in artificial intelligence for artificial metamaterials, Apl Mater. 12, 120602 (2024) [Google Scholar]
  50. S. Yin, H. Zhong, W. Huang, W. Zhang, J. Han, Deep learning enabled design of terahertz high-Q metamaterials. arXiv:2312.13986 [physics.optics] (2023) [Google Scholar]
  51. Y. Zhang, Z. Chen, H. Ge, K. Jia, Y. Jiang, Y. Bu, S. Wei, Intelligent design of terahertz metamaterial sensors based on a transformer encoder model, AIP Adv. 15, 085302 (2025) [Google Scholar]
  52. Y.H. Wang, C. Pang, Y.Z. Wang, J.R. Qi, Electromagnetic manipulation evolution from stacked meta-atoms to spatially cascaded metasurfaces, Annalen Der Physik 537, 2400158 (2025) [Google Scholar]
  53. Y. Cai, Y. Huang, N. Feng, Z. Huang, Improved transformer-based target matching of terahertz broadband reflective metamaterials with monolayer graphene, IEEE Trans. Microw. Theory Tech. 71, 3284 (2023) [Google Scholar]
  54. T.V. Tran, S.S. Nanthakumar, T. Rabczuk, X.Y. Zhuang, On-demand inverse design of metamaterials using deep neural networks with bayesian optimization, Intell. Comput. 4, 0139 (2025) [Google Scholar]
  55. Z.Y. Zhao, J. You, J. Zhang, S.Y. Du, Z.L. Tao, Y.H. Tang, T. Jiang, Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials, Nanophotonics 11, 4465 (2022) [Google Scholar]
  56. K.F. Ding, Y. Yang, Reverse design of load-bearing broadband metamaterial absorber assisted by deep learning, Smart Mater. Struct. 33, 125029 (2024) [Google Scholar]
  57. G.Q. Lan, Y. Wang, J.Y. Ou, Optimization of metamaterials and metamaterial-microcavity based on deep neural networks, Nanoscale Adv. 4, 5137 (2022) [Google Scholar]
  58. N.B. Roberts, M.K. Hedayati, A deep learning approach to the forward prediction and inverse design of plasmonic metasurface structural color, Appl. Phys. Lett. 119, 061101 (2021) [Google Scholar]
  59. J.J. Hou, H. Lin, W.L. Xu, Y.Z. Tian, Y. Wang, X.T. Shi, F. Deng, L.J. Chen, Customized inverse design of metamaterial absorber based on target-driven deep learning method, IEEE Access 8, 211849 (2020) [Google Scholar]
  60. H.Y. Yang, H.B. Lu, D.W. Shu, Y.Q. Fu, Multimodal origami shape memory metamaterials undergoing compression-twist coupling, Smart Mater. Struct. 32, 075013 (2023) [Google Scholar]
  61. P. Dorin, K.W. Wang, Broadband frequency and spatial on-demand tailoring of topological wave propagation harnessing piezoelectric metamaterials, Front. Mater. 7, 602996 (2021) [Google Scholar]
  62. M.A. Bessa, P. Glowacki, M. Houlder, Bayesian machine learning in metamaterial design: fragile becomes supercompressible, Adv. Mater. 31, 1904845 (2019) [Google Scholar]
  63. S.C. He, X.C. Yao, J. Tao, K. Wang, H.S. Tang, C.J. Wang, F. Gao, Z.J. Wang, J.Z. Song, Multifunctional reconfigurable electromagnetic metamaterials based on compression-torsion coupling structures for transmission modulations and holographic displays, Adv. Funct. Mater. 35, 2421065 (2025) [Google Scholar]
  64. L.D. Shao, W.R. Zhu, Recent advances in electromagnetic metamaterials and metasurfaces for polarization manipulation, J. Phys. D-Appl. Phys. 57, 343001 (2024) [Google Scholar]
  65. J.W. Liu, G.J. Lian, W.B. You, R.X. Zhang, Y.F. Cheng, C. Zhang, R.C. Che, Bayesian active learning for accelerated design of broadband polarization-insensitive metasurfaces, Intell. Comput. 4, 0135 (2025) [Google Scholar]
  66. M.A. Ardeshana, F.N. Thakkar, S.G. Domadia, S-band application: ultra-slim metamaterial absorber unit cell with dual-negative characteristics and polarization neutrality, J. Magn. Magn. Mater. 593, 171866 (2024) [Google Scholar]
  67. R. Fang, X. Zhang, B. Song, Z. Zhang, L. Zhang, J. Song, Y. Yao, M. Gao, K. Zhou, P. Wang, J. Lu, Y. Shi, 3D and 4D printing of electromagnetic metamaterials, Engineering 51, 171 (2025) [Google Scholar]
  68. D. Shin, J. Kim, I. Seo, K. Kim, Quasi-conformal transformation optics with elasto-electromagnetic metamaterials: design algorithm, Appl. Phys. Lett. 107, 021908 (2015) [Google Scholar]
  69. J.T. Hu, C. Zhang, X.K. Liu, H.D. Liu, G.Q. Liu, Design of a new open-type active electromagnetic metamaterial for magnetic field control based on numerical method, J. Appl. Phys. 137, 083101 (2025) [Google Scholar]
  70. X.C. Xing, X.Y. Tian, X.Y. Jia, D.C. Li, Reconfigurable liquid electromagnetic metamaterials driven by magnetic fields, Appl. Phys. Express 14, 041002 (2021) [Google Scholar]
  71. H.T. Chen, Compact fano-type liquid metamaterial resonator for high-precision temperature sensing, Front. Mater. 9, 941395 (2022) [Google Scholar]
  72. L. Ma, D.X. Chen, W.X. Zheng, J. Li, S.R. Zahra, Y.F. Liu, Y.D. Zhou, Y.J. Huang, G.J. Wen, Advanced electromagnetic metamaterials for temperature sensing applications, Front. Phys. 9, 657790 (2021) [Google Scholar]
  73. G.H. Feng, L.X. Guo, H.Y. Yu, Y. Li, B. Ren, B. Liu, L. Wan, J. Sun, X. He, Q.G. Fu, H.J. Li, J. Lu, Uniting superior electromagnetic wave absorption with high thermal stability in bioinspired metamaterial by direct ink writing, Adv. Funct. Mater. 35, 2424499 (2025) [Google Scholar]
  74. B.Y. Shan, Y. Shen, X.R. Yi, X.Q. Chi, K.J. Chen, Agile inverse design of polarization-independent multi-functional reconfiguration metamaterials based on doped VO2, Materials 17, 3534 (2024) [Google Scholar]
  75. C.C. Wang, X.Y. Lin, C.S. Hu, Y. Ding, Z.Q. Wang, Y.H. Zhou, X. Lin, J.T. Xu, Bio-based derived carbon materials for permittivity metamaterials: dual efficacy of electromagnetic wave protection and Joule heating, Mater. Horizons 12, 6349 (2025) [Google Scholar]
  76. C. Quan, S. Gu, J.L. Zou, C.C. Guo, W. Xu, Z.H. Zhu, J.F. Zhang, Phase change metamaterial for tunable infrared stealth and camouflage, Optics Express 30, 43741 (2022) [Google Scholar]
  77. Z.Y. Zheng, Q.W. Zhang, S.L. Ren, M. Lei, F.H. Zhang, P. Zhang, Y. Yu, H.Q. Wei, Stretchable conductive shape-memory nanocomposites for programmable electronics via photo-mediated multiscale structural design, Chem. Eng. J. 497, 154656 (2024) [Google Scholar]
  78. N. Zhang, M.M. Zhang, Q. Ouyang, L. Chen, Application of terahertz technology for chiral molecular sensing, Trac-Trends Anal. Chem. 191, 118360 (2025) [Google Scholar]
  79. T. Kaelberer, V.A. Fedotov, N. Papasimakis, D.P. Tsai, N.I. Zheludev, Toroidal dipolar response in a metamaterial, Science 330, 1510 (2010) [Google Scholar]
  80. J.Y. Hua, X.D. He, Broadband metamaterial linear polarization converter designed by a hybrid neural network with data augmentation, Aip Adv. 14, 095013 (2024) [Google Scholar]
  81. Y.M. Yan, F.J. Li, J.Q. Shen, M.Y. Zhuang, Y. Gao, W. Chen, Y.Y. Li, Z.L. Wu, Z.G. Dong, J.F. Zhu, Highly intelligent forward design of metamaterials empowered by circuit-physics-driven deep learning, Laser Photonics Rev. 19, 2400724 (2025) [Google Scholar]
  82. N. Kim, D. Lee, C. Kim, D.S. Lee, Y. Hong, Simple arithmetic operation in latent space can generate a novel three-dimensional graph metamaterials, Npj Comput. Mater. 10, 236 (2024) [Google Scholar]
  83. J.W. Li, A. Qinhua, Q.S. Lan, J.T. Yang, L.J. Yun, Y.L. Xia, C.F. Yang, Research on a demand design method of a cross polarization converter metasurface based on a depth generation model, Opt. Mater. Express 13, 2497 (2023) [Google Scholar]
  84. S.J. Niu, X.M. Liu, C.C. Wang, W.Z. Mu, W. Xu, Q. Wang, Breaking the trade-off between complexity and absorbing performance in metamaterials through intelligent design, Small 21, 202502828 (2025) [Google Scholar]
  85. S. Arulkumar, S. Gopinath, U.A. Kumar, H. Kraiem, Ultra-broadband metamaterial solar absorber based on S-shaped resonator design for enhanced industrial thermal energy harvesting with AI for behaviour prediction, Case Stud. Thermal Eng. 75, 107052 (2025) [Google Scholar]
  86. S. Saeed, M. Müftüoglu, G.R. Cheeran, T. Bocklitz, B. Fischer, M. Chemnitz, Nonlinear inference capacity of fiber-optical extreme learning machines, Nanophotonics 14, 2749 (2025) [Google Scholar]
  87. P. Figge, E. Anderson, K. Lewis, EXPRESS: AI-Human learning systems: investigating the strategic role of AI for organizational learning, Strateg. Organ. (2025). https://doi.org/10.1177/14761270251385860 [Google Scholar]
  88. W. Qu, Y. Wang, G. Li, Z. Xie, Z. Li, H. Deng, L. Shang, An adaptive assisted method based on MOPSO for THz MMA effective designing, J. Phys. D: Appl. Phys. 58, 035103 (2024) [Google Scholar]
  89. M. Kalogeropoulou, A. Kracher, P. Fucile, S.M. Mihaila, L. Moroni, Blueprints of architected materials: a guide to metamaterial design for tissue engineering, Adv. Mater. 36, 202408082 (2024) [Google Scholar]
  90. J. Gao, Y.M. Zhang, Q. Wu, All dielectric terahertz left-handed metamaterial based on Mie resonance coupling effects, IEEE Access 7, 94882 (2019) [Google Scholar]
  91. H. Zhang, L. Kang, S.D. Campbell, J.T. Young, D.H. Werner, Data driven approaches in nanophotonics: a review of AI-enabled metadevices, Nanoscale, 17, 23788 (2025) [Google Scholar]
  92. Z.D. Li, X.X. Wang, Z.G. Wang, X.W. Li, X. Yu, S. Ramakrishna, Y. Lu, L. Cheng, Emerging acousto-mechanical metamaterials: from physics-guided design to coupling-driven performance, Mater. Today 89, 151 (2025) [Google Scholar]
  93. A. Vakulenko, S. Kiriushechkina, M. Wang, M. Li, D. Zhirihin, X. Ni, S. Guddala, D. Korobkin, A. Alù, A.B. Khanikaev, Near‐field characterization of higher‐order topological photonic states at optical frequencies, Adv. Mater. 33, 202004376 (2021) [Google Scholar]
  94. A. Karnieli, Y. Li, A. Arie, The geometric phase in nonlinear frequency conversion, Front. Phys. 17, 12301 (2021) [Google Scholar]
  95. L.L. Yan, Y.S. Wang, W.C. Li, S. Yin, W. Huang, Z. Yin, D.M. Jia, Y.Q. Li, A bilayer array metamaterial based on silicon carbon foam/FeSiAl for broadband electromagnetic absorption, J. Alloys Compd. 954, 170129 (2023) [Google Scholar]
  96. J.J. Bai, M.L. Ge, J.N. Li, C.X. Tang, X.D. Sun, H.Y. Xing, S.J. Chang, Numerical investigation of broadband THz metamaterial absorber with double composite structure layer, Opt. Commun. 423, 63 (2018) [Google Scholar]
  97. Y.M. Wang, Z.K. Lei, X.C. Wang, Z.L. Chen, M.H. Lu, R.X. Bai, C. Yan, Mechano-electromagnetic coupling in negative Poisson's ratio metamaterials: dynamic deformation-driven multimodal wave absorption and integrated theoretical framework, Appl. Mater. Today 47, 102942 (2025) [Google Scholar]
  98. Y.Z. Ye, B. He, R. Liu, B. Sima, Z.W. Sun, Adaptive multi-modes absorption with enhanced electromagnetic environment compatibility, J. Appl. Phys. 134, 113103 (2023) [Google Scholar]
  99. W.W. Li, M.J. Chen, Z.H. Zeng, H. Jin, Y.M. Pei, Z. Zhang, Broadband composite radar absorbing structures with resistive frequency selective surface: optimal design, manufacturing and characterization, Compos. Sci. Technol. 145, 10 (2017) [Google Scholar]
  100. S. Mehdi, P. Tiwary, Thermodynamics-inspired explanations of artificial intelligence, Nat. Commun. 15, 7859 (2024) [Google Scholar]
  101. C. Zednik, Solving the black box problem: a normative framework for explainable artificial intelligence, Philos. Technol. 34, 265 (2019) [Google Scholar]
  102. J.B. Hopkins, Mechanical neural networks: an overview of self-learning metamaterials, J. Intell. Mater. Syst. Struct. 36, 1266 (2025) [Google Scholar]
  103. A.P. Garland, B.C. White, S.C. Jensen, B.L. Boyce, Pragmatic generative optimization of novel structural lattice metamaterials with machine learning, Mater. Des. 203, 109632 (2021) [Google Scholar]
  104. Y.J. Zheng, H.J. Dai, J.Y. Wu, C.P. Zhou, Z.W. Wang, R.G. Zhou, W.X. Li, Research progress and development trend of smart metamaterials, Front. Phys. 10, 1069722 (2022) [Google Scholar]
  105. M. Lei, J. Wang, G.L. Dai, P. Tan, J.P. Huang, Temperature-dependent transformation multiphysics and ambient-adaptive multiphysical metamaterials, EPL 135, 54003 (2021) [CrossRef] [EDP Sciences] [Google Scholar]
  106. Q. Ma, X.X. Gao, Z. Gu, C. Liu, L.L. Li, J.W. You, T.J. Cui, Intelligent neuromorphic computing based on nanophotonics and metamaterials, MRS Commun. 14, 1235 (2024) [Google Scholar]
  107. P. Sinha, T. Mukhopadhyay, Programmable multi-physical mechanics of mechanical metamaterials, Mater. Sci. Eng. R-Rep. 155, 100745 (2023) [Google Scholar]
  108. L. Huangchen, J.L. Peng, X. Yang, Z.P. Zhang, J. Zhou, Y.D. Hou, Classification principle enabled optimal frames for high-performance and intelligent polarimeters, Opt. Express 33, 12111 (2025) [Google Scholar]
  109. J. Chen, S. Hu, S. Zhu, T. Li, Metamaterials: from fundamental physics to intelligent design, Interdiscip. Mater. 2, 5 (2022) [Google Scholar]
  110. Z.X. Fu, H.N. Mao, B.L. Yin, Inverse design of lattice metamaterials for fully anisotropic elastic constants: a data-driven and gradient-based method, Compos. Struct. 359, 118975 (2025) [Google Scholar]
  111. B. Liu, L.J. Xu, Y.X. Wang, J.P. Huang, Diffusion model-based inverse design for thermal transparency, J. Appl. Phys. 135, 125101 (2024) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.