AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization

Artificial intelligence (AI) is fundamentally transforming materials discovery, shifting the paradigm from labor-intensive trial-and-error approaches to data-driven, automated workflows. This review examines emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows. We begin by surveying generative models for inverse materials design, tracing the evolution from early variational autoencoders and generative adversarial networks to recent diffusion models, Bayesian flow networks, flow-based architectures, transformer-based methods, and large language model-driven approaches for the de novo generation of crystalline materials with targeted properties. We then discuss physics-informed and data-efficient AI s…