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

Abstract

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 strategies that incorporate domain knowledge to enhance the model generalizability and robustness in low-data regimes. The emergence of multimodal foundation models, which integrate heterogeneous data modalities including crystal structures, text, and spectroscopic information into unified representations, is examined as a key enabler for cross task generalization. We further review AI-driven synthesis planning algorithms and autonomous self-driving laboratories that bridge the persistent gap between the computational design and experimental realization. Critical infrastructure challenges, including database limitations, FAIR data principles, and knowledge-graph construction, are also addressed. By highlighting both demonstrated capabilities and persistent limitations, this review aims to guide the reliable application of AI toward materials discovery workflows that connect candidate generation, synthesis feasibility, experimental feedback, and data provenance.