AI-driven inverse design of materials: Past, present and future

TL;DR

This paper reviews recent advancements in AI-driven inverse design of materials, highlighting how artificial intelligence techniques have transformed the discovery process by enabling efficient prediction and design of material structures and properties.

Contribution

It provides a comprehensive overview of the latest AI methods used in inverse material design, including background, key developments, and future challenges.

Findings

AI enables effective characterization of material-structure-property relationships

Generative models have significantly advanced inverse material design

The survey identifies key challenges and future directions in the field

Abstract

The discovery of advanced materials is the 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 a large number of 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 technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on 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 issues 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.