Abstract
With the increasing intelligence of electronic information systems, urgent demand exists for high-performance customized electromagnetic shielding materials. However, traditional research and development paradigms are constrained by critical bottlenecks, like multi-parameter coupling, high trial-and-error costs, and multi-scale design hurdles, failing to satisfy efficient material development needs. Artificial intelligence (AI), leveraging its core advantages of data-driven approaches and algorithmic optimization, offers a transformative paradigm to overcome these bottlenecks. Although AI-driven methodologies have demonstrated tremendous potential in the design of electromagnetic shielding materials, systematic barriers from inadequate interdisciplinary collaboration still limit its technological empowerment. In response to these challenges, we highlight the following solutions to advance AI-enabled electromagnetic shielding material design: (1) developing neural network models driven by data-physics integration, (2) developing domain-specific large language models for electromagnetic shielding materials, (3) establishing comprehensive databases for electromagnetic shielding materials, and (4) promoting domain-specific data sharing and the construction of standardized protocols.