Advances in Intelligent Design of Metamaterials

Metamaterials are artificially synthesized materials whose structures are meticulously designed and precisely controlled to exhibit unique properties not found in natural materials. These functional metamaterials have significant applications in fields such as optics, electromagnetics, acoustics, and mechanics. However, traditional metamaterial design often relies on human expertise, resulting in lengthy design cycles, a lack of flexibility, and limited tunability, which hinder the rapid design and mass production of metamaterials. Efficient design and performance tuning of metamaterials have become crucial research directions in mechanical engineering, and materials science. In recent years, with the rapid development of artificial intelligence algorithms, the application of intelligent algorithms in metamaterial design has been expanding, demonstrating advantages such as efficient optimization, generation of diverse structures, and shortened design cycles. This paper first introduces the basic concepts and development history of meta materials, and then discusses the application fields and design issues of metamaterials from the perspective of practical needs. The paper identifies the core issues of intelligent design algorithms as data representation and dataset construction, providing a detailed explanation and comparative analysis of these two aspects. Additionally, it reviews the framework of intelligent optimization algorithms. Finally, this paper highlights the challenges in metamaterial design, including the scarcity of high-quality datasets, the complexity of multi-objective optimization, and the computational demands of high-resolution metamaterial analysis. Additionally, it explores future trends in the field, emphasizing the development of metamaterials that are “expressible” “editable” “analyzable” “optimizable” and “manufacturable” to address diverse functional requirements.