Advances in artificial intelligence for artificial metamaterials

Abstract

The 2024 Nobel Prizes in Physics and Chemistry were awarded for foundational discoveries and inventions enabling machine learning through artificial neural networks. Artificial intelligence (AI) and artificial metamaterials are two cutting-edge technologies that have shown significant advancements and applications in various fields. AI, with its roots tracing back to Alan Turing’s seminal work, has undergone remarkable evolution over decades, with key advancements including the Turing Test, expert systems, deep learning, and the emergence of multimodal AI models. Electromagnetic wave control, critical for scientific research and industrial applications, has been significantly broadened by artificial metamaterials. This review explores the synergistic integration of AI and artificial metamaterials, emphasizing how AI accelerates the design and functionality of artificial materials, while novel physical neural networks constructed from artificial metamaterials significantly enhance AI’s computational speed and its ability to solve complex physical problems. This paper provides a detailed discussion of AI-based forward prediction and inverse design principles and applications in metamaterial design. It also examines the potential of big-data-driven AI methods in addressing challenges in metamaterial design. In addition, this review delves into the role of artificial metamaterials in advancing AI, focusing on the progress of electromagnetic physical neural networks in optics, terahertz, and microwaves. Emphasizing the transformative impact of the intersection between AI and artificial metamaterials, this review underscores significant improvements in efficiency, accuracy, and applicability. The collaborative development of AI and artificial metamaterials accelerates the metamaterial design process and opens new possibilities for innovations in photonics, communications, radars, and sensing.