From High-Throughput Screening to Generative Design: Artificial Intelligence-Driven Dielectric Materials Discovery

). Traditional high-throughput screening is limited to existing chemical spaces and cannot resolve the inherent orthogonality between key dielectric metrics and multiphysics coupling conflicts. This review systematically maps the paradigm shift in dielectric research from passive empirical screening toward generative, AI-driven autonomous discovery. We first examine the multiscale physical origins of the core dielectric performance trade-offs and the challenge of high-fidelity data scarcity, highlighting the role of physics-informed descriptors in bridging atomic-scale structures and macroscopic properties. We then survey the evolution of high-fidelity surrogate models and multiobjective optimization frameworks for efficient structure-property mapping followed by analysis of mainstream generative architectures (variational autoencoders, VAEs; generative adversarial networks, GANs; diffusion models), which enable the inverse design of dielectric polymers and inorganic crystals beyond known chemistries. Furthermore, we discuss the integration of self-driving autonomous laboratories and active learning strategies to close the feedback loop between computational prediction and experimental validation. Finally, we address the unresolved barriers in physics embedding, model explainability, and experimental synthesizability, outlining an actionable roadmap toward Physics-Sovereign AI and large language model (LLM)-driven digital scientists for dielectric innovation. This review provides a holistic, application-focused perspective on the transition from Edisonian trial-and-error approaches to a new era of rational, accelerated dielectric materials development and delivers a practical decision-making framework for researchers selecting AI tools for targeted dielectric design.