Advances and perspectives in artificial intelligence-empowered electromagnetic protection materials research
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摘要: 面对日趋智能化的电子信息系统对高性能、定制化电磁防护材料的迫切需求，传统研发模式受限于多参数耦合复杂、试错成本高、跨尺度设计难等瓶颈，难以适应高效研发需求。人工智能（AI）通过数据驱动与算法优化，为突破上述瓶颈提供了新范式。系统综述了AI赋能电磁防护材料相关研究，首先剖析电磁防护材料研发主要特点与核心挑战，阐明AI应用于该领域的高适配性；随后从正向预测和逆向设计两方面分述该领域典型案例；最后指出在数据层面、物理可解释性和应用推广方面存在的挑战，并分别从构建电磁防护材料基因库、发展数据物理融合驱动神经网络以及推动领域数据共享、构建标准化协议三方面提出具体思考，为下一代电磁防护材料的智能化提供方向。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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