AI and Computing for Active Metamaterial Design: A Perspective

Abstract Active metamaterials exhibit unique and tunable functionalities by altering their shape and material properties in response to external stimuli. While this enables the creation of material systems that can respond to dynamically changing environments, it also necessitates the development of innovative computational strategies for active metamaterial design. Recent advancements in computational algorithms, such as physics-based optimization techniques and artificial intelligence, have offered new opportunities and attracted significant interest in the field. In this perspective, we examine how these advancements are shaping the development of metamaterial systems. First, we discuss the different levels of complexity for metamaterial design, categorized by the material system (passive vs. active) and design approach (forward prediction vs. inverse design). We then provide an overview of recent efforts aimed at overcoming the challenges presented by design problems of increasing difficulty. Finally, current limitations and possible future directions for the field are discussed, emphasizing the importance of active property tuning and multifunctionality. This paper is expected to provide a comprehensive overview of the current computational landscape, offer insights into emerging strategies for active metamaterial design, and guide future research toward more programmable, multifunctional material systems.