Advances and perspectives in artificial intelligence-empowered electromagnetic protection materials research

Yao Lixiang; Huang Xianjun; Chen Hongting; Liu Peiguo

doi:10.11884/HPLPB202537.250188

Volume 37 Issue 8

Jul. 2025

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Yao Lixiang, Huang Xianjun, Chen Hongting, et al. Advances and perspectives in artificial intelligence-empowered electromagnetic protection materials research[J]. High Power Laser and Particle Beams, 2025, 37: 089001. doi: 10.11884/HPLPB202537.250188

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Yao Lixiang, Huang Xianjun, Chen Hongting, et al. Advances and perspectives in artificial intelligence-empowered electromagnetic protection materials research[J]. High Power Laser and Particle Beams, 2025, 37: 089001. doi: 10.11884/HPLPB202537.250188

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Advances and perspectives in artificial intelligence-empowered electromagnetic protection materials research

doi: 10.11884/HPLPB202537.250188

College of Electronics Science and Technology, National University of Defense Technology, Changsha 410073, China

Received Date: 2025-06-26
Accepted Date: 2025-07-25
Rev Recd Date: 2025-07-25

Available Online: 2025-07-25

Publish Date: 2025-07-26

Abstract

Abstract

Facing the urgent demand for high-performance, customized electromagnetic protection materials driven by increasingly intelligent electronic information systems, traditional research and development (R&D) models face severe limitations due to complex multi-parameter coupling, high trial-and-error costs, and difficulties in cross-scale design, hindering their ability to meet the need for efficient R&D. Artificial intelligence (AI), leveraging data-driven approaches and algorithmic optimization, offers a transformative paradigm to overcome these limitations. This paper systematically reviews AI-empowered research in electromagnetic protection materials. It begins by analyzing the key characteristics and core challenges in the R&D of these materials, highlighting the high suitability of AI for this domain. Subsequently, it illustrates representative research cases from both forward prediction and inverse design perspectives within the field. Finally, the paper identifies existing challenges concerning data availability, physical interpretability of AI models, and practical application deployment barriers. Specific considerations are proposed in three aspects: constructing specialized electromagnetic material gene databases, developing physics-informed neural networks that integrate data with physical principles, and emphasizing the need to promote domain-specific data sharing and establish standardized protocols, so as to pave the way for the intelligent development of next-generation electromagnetic protection materials.

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