Artificial Intelligence-Assisted Development of Flexible Ferroelectric Materials for Emerging Electronic and Sensor Applications
Keywords:
artificial intelligence; ferroelectric materials; flexible electronics; materials informatics; data infrastructure; sustainability; policyAbstract
Flexible ferroelectric materials occupy a critical position in next-generation electronic and sensor systems because they combine switchable polarization, piezoelectric response, and mechanical deformability. However, their development is constrained by complex processing requirements, substrate compatibility limits, fatigue under cyclic strain, and the need to preserve functional properties across multiple length scales. Artificial intelligence has emerged as a powerful instrument for navigating this complexity, but its effective application requires more than predictive modeling. This paper presents a system-level analysis of artificial intelligence-assisted development of flexible ferroelectric materials for emerging electronic and sensor applications. It examines structural trade-offs among ferroelectric performance, mechanical flexibility, process controllability, and manufacturing scalability. The discussion emphasizes data infrastructure, interoperability, autonomous experimentation, governance, robustness, fairness, and lifecycle sustainability. Rather than treating machine learning as a standalone optimization tool, the paper argues that durable progress depends on integrating algorithmic systems with experimental automation, institutional data governance, and responsible innovation frameworks. A central concern is the alignment between model-driven recommendations and physical realities such as thermal budgets, bending-induced depolarization, electrode adhesion, and long-term operational reliability. The analysis further explores deployment pathways in wearable sensors, conformable memory, soft robotics, and distributed environmental monitoring. It concludes that sustainable artificial intelligence-assisted materials development requires a coordinated socio-technical architecture that balances predictive power, experimental validation, equitable access to research infrastructure, and environmental accountability.
References
1. 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
2. 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(1), 54. https://doi.org/10.1038/s41524-017-0056-5
3. Martin, L. W., & Rappe, A. M. (2017). Thin-film ferroelectric materials and their applications. Nature Reviews Materials, 2(2), 16087. https://doi.org/10.1038/natrevmats.2016.87
4. Scott, J. F. (2007). Applications of modern ferroelectrics. Science, 315(5814), 954–959. https://doi.org/10.1126/science.1129564
5. Gao, W., Zhu, Y., Wang, Y., Yuan, G., & Liu, J.-M. (2020). A review of flexible perovskite oxide ferroelectric films and their applications. Journal of Materiomics, 6(1), 1–16. https://doi.org/10.1016/j.jmat.2019.11.001
6. Haertling, G. H. (1999). Ferroelectric ceramics: History and technology. Journal of the American Ceramic Society, 82(4), 797–818. https://doi.org/10.1111/j.1151-2916.1999.tb01840.x
7. Damjanovic, D. (1998). Ferroelectric, dielectric and piezoelectric properties of ferroelectric thin films and ceramics. Reports on Progress in Physics, 61(9), 1267–1324. https://doi.org/10.1088/0034-4885/61/9/002
8. 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
9. 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(1), 21. https://doi.org/10.1038/s41524-019-0153-8
10. Luan, S., He, H., Zhang, L., Yu, J., Cao, X., Wang, J., ... & Sun, R. (2025). Fabrication of High Performance BaTiO3 Films via Aqueous Tape Casting for ultrathin MLCC applications. Ceramics International.
11. Kusne, A. G., Yu, H., Wu, C., Zhang, H., Hattrick-Simpers, J., DeCost, B., Sarker, S., Oses, C., Toher, C., Curtarolo, S., Davydov, A. V., Agarwal, R., Bendersky, L. A., Li, M., Mehta, A., & Takeuchi, I. (2020). On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications, 11(1), 5966. https://doi.org/10.1038/s41467-020-19597-w
12. Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A. (2013). Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 1(1), 011002. https://doi.org/10.1063/1.4812323
13. 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
14. De Pablo, J. J., Jackson, N. E., Webb, M. A., Chen, L.-Q., Moore, J. E., Morgan, D., Jacobs, R., Pollock, T. M., Schlom, D. G., Toberer, E. S., Analytis, J., Dabo, I., DeLongchamp, D. M., Fazzio, A., Gregoire, J. M., Hautier, G., Jones, T. E., Lookman, T., Marzari, N., ... Hawker, C. J. (2019). New frontiers for the materials genome initiative. npj Computational Materials, 5(1), 41. https://doi.org/10.1038/s41524-019-0173-4
15. Batra, R., Song, L., & Ramprasad, R. (2021). Emerging materials intelligence ecosystems propelled by machine learning. Nature Reviews Materials, 6(8), 655–678. https://doi.org/10.1038/s41578-020-00255-y
16. Maffettone, P. M., Friederich, P., Baird, S. G., Blaiszik, B., Brown, K. A., Campbell, S. I., ... & Aspuru-Guzik, A. (2023). The need for structured data in autonomous materials discovery. Matter, 6(10), 3311–3327. https://doi.org/10.1016/j.matt.2023.09.011
17. 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(1), 83. https://doi.org/10.1038/s41524-019-0221-0
18. Stanev, V., Oses, C., Kusne, A. G., Rodriguez, E., Paglione, J., Curtarolo, S., & Takeuchi, I. (2018). Machine learning modeling of superconducting critical temperature. npj Computational Materials, 4(1), 29. https://doi.org/10.1038/s41524-018-0085-8
19. Morgan, D., & Jacobs, R. (2020). Opportunities and challenges for machine learning in materials science. Annual Review of Materials Research, 50, 71–103. https://doi.org/10.1146/annurev-matsci-070218-010015
20. Vasudevan, R. K., Choudhary, K., Mehta, A., Smith, R., Kusne, G., Tavazza, F., Vlcek, L., Ziatdinov, M., Kalinin, S. V., & Hattrick-Simpers, J. (2019). Materials science in the artificial intelligence age: High-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics. MRS Communications, 9(3), 821–838. https://doi.org/10.1557/mrc.2019.95
21. Suh, C., Fare, C., Warren, J. A., & Pyzer-Knapp, E. O. (2020). Evolving the materials genome: How machine learning is fueling a new generation of materials discovery. Annual Review of Materials Research, 50, 1–25. https://doi.org/10.1146/annurev-matsci-082019-105100
22. Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C. J., Maruyama, B., Persson, K. A., & AspuruGuzik, A. (2018). Accelerating the discovery of materials for clean energy in the era of smart automation. Nature Reviews Materials, 3(5), 5–20. https://doi.org/10.1038/s41578-018-0005-z
23. Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019). Next-generation experimentation with self-driving laboratories. Trends in Chemistry, 1(3), 282–291. https://doi.org/10.1016/j.trechm.2019.02.007
24. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
25. Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568–1580. https://doi.org/10.1016/j.respol.2013.05.008
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
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.
