Crystal Structure Prediction Meets Artificial Intelligence

Crystal structure prediction (CSP) represents a fundamental research frontier in computational materials science and chemistry, aiming to predict thermodynamically stable periodic structures from given chemical compositions. Traditional methods often face challenges such as high computational costs and local minima trapping. Recently, artificial intelligence methods, represented by generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and large language models (LLMs), have revolutionized the traditional prediction paradigm. These computational frameworks efficiently extract chemical rules and structural features from crystal databases, significantly reducing computational costs while maintaining prediction accuracy. This Perspective systematically evaluates the advantages and limitations of various generative models, explores their synergies with conventional approaches, and discusses their future prospects in accelerating materials discovery and development, providing new insights for future research directions.