AI-Driven Inverse Design of Materials: Past, Present, and Future

Abstract The discovery of advanced materials is a cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through numerous experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic-structure calculation methods, such as the density functional theory and high-throughput computational methods. Recently, the rapid development of artificial intelligence (AI) technology in computer science has enabled the effective characterization of the implicit association between material properties and structures, thus forming an efficient paradigm for the inverse design of functional materials. Significant progress has been achieved in the inverse design of materials based on generative and discriminative models, attracting widespread interest from researchers. Considering this rapid technological progress, in this survey, we examine the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining challenges for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers.