Machine Learning-Assisted Prediction of Dielectric Properties in BaTiO₃-Based Electronic Ceramics

Abstract

The prediction of dielectric properties in barium titanate based electronic ceramics has traditionally relied on iterative experimental campaigns, phenomenological models, and domain expertise. Machine learning now offers a complementary route for learning complex property-processing-composition relationships from heterogeneous materials data. This paper presents a system-level examination of machine learning-assisted dielectric property prediction for BaTiO3-based ceramics, with particular attention to multilayer ceramic capacitor applications. Rather than focusing solely on predictive accuracy, the analysis integrates data infrastructure, experimental knowledge representation, model architecture selection, feature engineering, deployment strategy, robustness, governance, and sustainability. The discussion highlights structural trade-offs between model interpretability and predictive flexibility, between centralized data platforms and proprietary laboratory workflows, and between short-term model performance and long-term reliability. The paper further examines how uncertainty quantification, domain-informed descriptors, and active learning can improve generalization under limited experimental data. Governance and policy dimensions, including data sharing, reproducibility, algorithmic fairness, and environmental impact, are treated as first-order system requirements rather than secondary concerns. The analysis draws on cross-domain lessons from materials informatics, high-throughput computation, and industrial deployment of data-driven systems. It concludes that sustainable advances in dielectric property prediction require coordinated investment in curated data resources, interpretable yet expressive machine learning architectures, uncertainty-aware deployment pipelines, and governance mechanisms that balance openness with commercial confidentiality.