Functional ceramics are a class of key inorganic, non-metallic materials that can couple with and respond to electric, magnetic, mechanical, thermal and optical fields. They underpin a wide range of advanced technologies, including energy conversion, information storage and intelligent sensing. As high-end equipment and frontier science develop rapidly worldwide, the demands placed on the performance of functional ceramics are increasing, pushing materials design towards increasingly extreme compositional, structural and processing conditions. However, progress in this field has long been constrained by the intertwined challenges of multiple objectives, scales and fields. Firstly, the performance of materials and the feasibility of processing are often in strong competition with each other. This means that the design is inherently a problem of multi-objective trade-offs and optimization within a restricted feasible space. Secondly, macroscopic functional responses are regulated by factors spanning multiple length scales, including electronic structure, lattice characteristics, and microstructure. Feedback across these scales can greatly amplify the effect of local perturbations on properties. Thirdly, functional ceramics may undergo nonlinear and irreversible evolution under the combined action of high electric fields, elevated temperatures and mechanical loading, further complicating performance evaluation and controllable design. In recent years, AI for materials has emerged as an important paradigm for addressing complex design problems. Through the integration of machine learning and automation, substantial advances have been achieved in metals, polymers and composites, including property prediction, structure-property correlation analysis and inverse design. For functional ceramics, field coupling, defect sensitivity and process dependence make them a particularly challenging yet highly promising target for AI-enabled research. Against this backdrop, a systematic review of recent progress in AI-driven functional ceramics research, outlining how AI is reshaping the broader material design, is important for accelerating the intelligent design of functional ceramics.

AI-enabled research in functional ceramics can be understood through three connected layers: data, model and application. At the data level, the properties of functional ceramics are jointly governed by composition, microstructure and processing history, rendering the associated datasets intrinsically heterogeneous and complex. Composition is typically represented by stoichiometry and dopant species; microstructural information spans multiple hierarchical levels, from lattice distortion and domain structures to grain size and pore morphology; process data are strongly path dependent and require continuous descriptions of heating, cooling and atmosphere switching; and response data are expressed not only as scalar quantities, such as dielectric, piezoelectric and thermoelectric metrics, but also as spectra, loops and other unstructured responses. Accordingly, the foundation of AI for functional ceramics lies in the construction of high-quality, multimodal and traceable datasets, supported by high-throughput techniques, knowledge extraction and multi-fidelity data fusion to improve both data quantity and quality. At the model level, current studies are converging on an integrated framework comprising prediction, uncertainty, generation and decision models. Prediction models establish forward mappings among composition, processing, structure and properties; uncertainty models support out-of-distribution prediction and reliability assessment; generation models enable inverse searches conditioned on target properties; and decision models combine experimental cost and information gain to guide closed-loop optimization. Within this framework, robustness and interpretability have emerged as central requirements: models could generalize across different systems, processing routes and testing conditions, while physically informed descriptors and mechanistic correlations are needed to reveal the factors that govern functional responses. At the application level, AI has increasingly permeated the full design workflow, giving rise to surrogate modelling, demand-driven exploration, multi-objective optimization and closed-loop pipeline, and thereby creating a more targeted, iterative and efficient design paradigm.

AI is reshaping the design workflow of functional ceramics through three major directions: intelligent inverse design, cross-scale intelligent computation and large-language-model-driven research platforms. First, intelligent inverse design targets on-demand materials design by integrating multiple representations of composition, processing, microstructure and property with uncertainty quantification, physical feasibility constraints and active learning, thereby establishing a closed loop spanning data alignment, forward prediction, inverse design and experimental validation. This strategy can improve the hit rate, manufacturability and reproducibility of proposed candidates. Second, cross-scale intelligent computation leverages machine learning to bridge models at different scales, including electronic structure, atomic dynamics, mesoscale microstructural evolution and continuum responses. By retaining key physical constraints while reducing computational cost, such approaches enable density functional theory, machine-learning interatomic potentials, phase-field models and finite-element analysis to operate within a unified framework for materials design. Third, large-language-model-driven research platforms are evolving from tools for knowledge extraction and literature analysis into collaborative hubs that connect knowledge, computation, decision and experimental execution. By integrating information from papers, patents, laboratory records and process documentation, large language models can assist in building high quality datasets, extracting process pathways and failed experiment information, and coupling with agentic systems and automated experimental platforms, thus paving the way for self-driving closed-loop laboratories in functional ceramics research.