Artificial Intelligence for Composition–Structure–Property Optimization of Ferroelectric Ceramics
Keywords:
ferroelectric ceramics; materials informatics; composition–structure–property relationships; Bayesian optimization; autonomous experimentation; governance; sustainabilityAbstract
Ferroelectric ceramics occupy a critical position in modern electronics, sensing, actuation, energy storage, and electromechanical transduction, yet their development remains constrained by complex coupling among chemical composition, crystallographic structure, microstructure, processing history, and functional property response. Artificial intelligence offers a powerful means to navigate this high-dimensional design space, but its effective use requires more than isolated predictive models. It demands integrated computational, experimental, data, and governance architectures that can support reproducible discovery, deployment, and lifecycle management. This paper examines artificial intelligence for composition–structure–property optimization of ferroelectric ceramics from a system-level perspective. It argues that progress depends on aligning machine learning architectures with physical priors, multi-fidelity characterization, automated experimentation, and robust data infrastructure. The discussion considers structural trade-offs among accuracy, interpretability, throughput, cost, and sustainability, as well as the institutional arrangements needed to ensure fairness, reproducibility, and policy coherence. Case illustrations include perovskite ferroelectrics, barium titanate-based dielectrics, lead-free piezoelectric systems, and multilayer ceramic capacitor manufacturing. The paper concludes that artificial intelligence can accelerate ferroelectric ceramic design only when treated as a socio-technical capability embedded in material science infrastructure rather than as a standalone algorithmic substitute for domain expertise. Such an approach can improve discovery outcomes while preserving scientific accountability and enabling responsible deployment.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
