Machine Learning-Driven Prediction of Dielectric Properties in Advanced Electronic Ceramics
Keywords:
machine learning; electronic ceramics; dielectric properties; multilayer ceramic capacitors; materials informatics; robustness; governance; sustainabilityAbstract
Advanced electronic ceramics are central to capacitors, sensors, actuators, and high-frequency communication systems, where dielectric permittivity, loss, breakdown strength, and temperature stability determine device performance and reliability. Machine learning has emerged as a powerful approach for predicting these properties, yet the most consequential challenges are not merely algorithmic. They concern data infrastructure, experimental integration, reproducibility, deployment constraints, robustness under distribution shift, fairness across laboratories and material systems, and governance across industrial and public research ecosystems. This paper examines machine learning-driven prediction of dielectric properties in advanced electronic ceramics from a system-level perspective. It first situates ceramic dielectrics within the broader context of ferroelectric physics, multilayer ceramic capacitor manufacturing, lead-free material development, and high-temperature polymer-ceramic composites. It then analyzes model architectures, including descriptor-based regression, graph representations, neural network potentials, and active learning, while emphasizing that predictive accuracy must be balanced against interpretability, computational cost, and experimental validation. The discussion extends to data infrastructure, metadata standards, model documentation, auditing, and concept drift, arguing that dielectric property prediction should be treated as a socio-technical infrastructure rather than a standalone computational task. Sustainability and policy implications are considered, including raw material criticality, sintering energy, open data, and equitable access to high-throughput facilities. Case illustrations from solid electrolytes, polymer dielectrics, and multilayer ceramic capacitors clarify how cross-domain lessons can inform ceramic dielectric design. The paper concludes that robust, fair, and sustainable machine learning for electronic ceramics requires integrated architectures that couple models, instruments, materials databases, human expertise, and governance mechanisms from the outset.
References
1. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: Recent applications and prospects. npj Computational Materials, 3, 54. https://doi.org/10.1038/s41524-017-0056-5
2. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2
3. Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5, 83. https://doi.org/10.1038/s41524-019-0221-0
4. Behler, J. (2021). Four generations of high-dimensional neural network potentials. Chemical Reviews, 121(16), 10037–10072. https://doi.org/10.1021/acs.chemrev.0c00868
5. Deringer, V. L., Caro, M. A., & Csányi, G. (2019). Machine learning interatomic potentials as emerging tools for materials science. Advanced Materials, 31(46), 1902765. https://doi.org/10.1002/adma.201902765
6. Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T., & Ramprasad, R. (2016). Machine learning strategy for accelerated design of polymer dielectrics. Scientific Reports, 6, 20952. https://doi.org/10.1038/srep20952
7. Kim, C., Pilania, G., & Ramprasad, R. (2016). From organized high-throughput data to phenomenological theory using machine learning: The example of dielectric breakdown. Chemistry of Materials, 28(5), 1304–1311. https://doi.org/10.1021/acs.chemmater.5b04109
8. Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013). Accelerating materials property predictions using machine learning. Scientific Reports, 3, 2810. https://doi.org/10.1038/srep02810
9. Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016). Machine-learning-assisted materials discovery using failed experiments. Nature, 533(7601), 73–76. https://doi.org/10.1038/nature17439
10. Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2017). Holistic computational structure screening of more than 12,000 candidates for solid lithium-ion conductor materials. Energy & Environmental Science, 10(1), 306–320. https://doi.org/10.1039/C6EE02697D
11. Luan, Saiwei, et al. "Fabrication of High Performance BaTiO3 Films via Aqueous Tape Casting for ultrathin MLCC applications." Ceramics International (2025).
12. Cohen, R. E. (1992). Origin of ferroelectricity in perovskite oxides. Nature, 358(6382), 136–138. https://doi.org/10.1038/358136a0
13. Scott, J. F. (2007). Applications of modern ferroelectrics. Science, 315(5814), 954–959. https://doi.org/10.1126/science.1129564
14. Kishi, H., Mizuno, Y., & Chazono, H. (2003). Base-metal electrode-multilayer ceramic capacitors: Past, present and future perspectives. Japanese Journal of Applied Physics, 42(1R), 1–15. https://doi.org/10.1143/JJAP.42.1
15. Pan, M. J., & Randall, C. A. (2010). A brief introduction to ceramic capacitors. IEEE Electrical Insulation Magazine, 26(3), 44–50. https://doi.org/10.1109/MEI.2010.5482787
16. Yang, L., Kong, X., Li, F., Hao, H., Cheng, Z., Liu, H., Li, J.-F., & Zhang, S. (2019). Perovskite lead-free dielectrics for energy storage applications. Progress in MaterialsScience, 102, 72–108. https://doi.org/10.1016/j.pmatsci.2018.12.005
17. Li, Q., Chen, L., Gadinski, M. R., Zhang, S., Zhang, G., Li, H. U., Iagodkine, E., Haque, A., Chen, L.-Q., Jackson, T. N., & Wang, Q. (2015). Flexible high-temperature dielectric materials from polymer nanocomposites. Nature, 523(7562), 576–579. https://doi.org/10.1038/nature14647
18. Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data-driven materials science: Status, challenges, and perspectives. Advanced Science, 6(21), 1900808. https://doi.org/10.1002/advs.201900808
19. Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020). A critical review of machine learning of energy materials. Advanced Energy Materials, 10(8), 1903242. https://doi.org/10.1002/aenm.201903242
20. Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019). Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Computational Materials, 5, 21. https://doi.org/10.1038/s41524-019-0153-8
21. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873
22. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596
23. Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 44. https://doi.org/10.1145/2523813
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