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

2024-11-14

Chinese Physics Letters

AI Summary

Addressing the inverse design challenge posed by strong nonlinear relationships between material structure and properties, this paper systematically reviews recent advances in artificial intelligence (AI)-driven inverse design of functional materials. Methodologically, it focuses on the core task of “inferring structure from target properties,” integrating generative models (e.g., VAEs, GANs, diffusion models) with discriminative models, while innovatively incorporating physics-informed constraints—such as density functional theory (DFT) and high-throughput computations—alongside graph neural networks (GNNs) to establish an interpretable, generalizable, multiscale modeling framework. The work establishes, for the first time, a coherent technological evolution pathway for AI-based inverse design, synthesizes prevailing paradigms and identifies critical bottlenecks, and proposes a methodology that harmonizes data-driven learning with physical consistency. These contributions deliver a reusable theoretical framework and a forward-looking research roadmap for the efficient discovery of novel functional materials.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• AI-driven inverse design of materials
• Characterizing material properties and structures
• Overcoming challenges in material discovery

Innovation

• AI-driven inverse design
• Generative and discriminative models
• Electronic structure calculations