REFERENCES

1. Choo, S.; Varshney, S.; Liu, H.; Sharma, S.; James, R. D.; Jalan, B. From oxide epitaxy to freestanding membranes: opportunities and challenges. Sci. Adv. 2024, 10, eadq8561.

2. Wang, S.; Li, W.; Deng, C.; et al. Giant electric field-induced second harmonic generation in polar skyrmions. Nat. Commun. 2024, 15, 1374.

3. Yang, X.; Han, L.; Ning, H.; et al. Ultralow-pressure-driven polarization switching in ferroelectric membranes. Nat. Commun. 2024, 15, 9281.

4. Dou, L.; Yang, B.; Ye, X.; et al. Flexible high-entropy functional ceramics. Nat. Commun. 2025, 16, 5915.

5. Liu, K.; Jin, F.; Zhu, T.; et al. Interface-controlled uniaxial in-plane ferroelectricity in Hf_0.5Zr_0.5O₂(100) epitaxial thin films. Nat. Commun. 2025, 16, 7385.

6. Ma, C.; Luo, Z.; Huang, W.; et al. Sub-nanosecond memristor based on ferroelectric tunnel junction. Nat. Commun. 2020, 11, 1439.

7. Liu, C.; Si, Y.; Zhang, H.; et al. Low voltage-driven high-performance thermal switching in antiferroelectric PbZrO₃ thin films. Science 2023, 382, 1265-9.

8. Yang, B.; Zhang, Q.; Huang, H.; et al. Engineering relaxors by entropy for high energy storage performance. Nat. Energy. 2023, 8, 956-64.

9. Guo, Y.; Peng, B.; Lu, G.; et al. Remarkable flexibility in freestanding single-crystalline antiferroelectric PbZrO₃ membranes. Nat. Commun. 2024, 15, 4414.

10. Qian, J.; Liu, Y.; He, L.; et al. Topological bubble domain engineering for high strain response. Sci. Adv. 2025, 11, eadw8840.

11. Ren, Z.; Deng, S.; Shao, J.; et al. Ultrahigh-power-density flexible piezoelectric energy harvester based on freestanding ferroelectric oxide thin films. Nat. Commun. 2025, 16, 3192.

12. Yao, Y.; Liu, H.; Hu, Y.; et al. Fluctuating local polarization: a generic fingerprint for enhanced piezoelectricity in Pb-based and Pb-free perovskite ferroelectrics. Nat. Commun. 2025, 16, 7442.

13. Tao, A.; Jiang, Y.; Chen, S.; et al. Ferroelectric polarization and magnetic structure at domain walls in a multiferroic film. Nat. Commun. 2024, 15, 6099.

14. Pan, Q.; Xiong, Y. A.; Sha, T. T.; et al. Strain-induced tunable enhancement of piezoelectricity in a novel molecular multiferroic material. Adv. Mater. 2025, 37, e2410585.

15. Trier, F.; Noël, P.; Kim, J.; Attané, J.; Vila, L.; Bibes, M. Oxide spin-orbitronics: spin–charge interconversion and topological spin textures. Nat. Rev. Mater. 2022, 7, 258-74.

16. Gao, W.; Zhu, Y.; Wang, Y.; Yuan, G.; Liu, J. A review of flexible perovskite oxide ferroelectric films and their application. J. Materiomics. 2020, 6, 1-16.

17. Xu, R.; Liu, S.; Saremi, S.; et al. Kinetic control of tunable multi-state switching in ferroelectric thin films. Nat. Commun. 2019, 10, 1282.

18. Fan, Y.; Zhang, S.; Xue, Z.; et al. Hidden structural phase transition assisted ferroelectric domain orientation engineering in Hf_0.5Zr_0.5O₂ films. Nat. Commun. 2025, 16, 4232.

19. Hafner, J.; Benaglia, S.; Richheimer, F.; et al. Multi-scale characterisation of a ferroelectric polymer reveals the emergence of a morphological phase transition driven by temperature. Nat. Commun. 2021, 12, 152.

20. Yao, S.; Jiang, H.; Wen, J.; et al. Electrically modulated photothermal force microscopy for revealing molecular conformation changes during polarization switching at the nanoscale. Nat. Commun. 2025, 16, 6680.

21. Cai, J.; Chu, X.; Xu, K.; Li, H.; Wei, J. Machine learning-driven new material discovery. Nanoscale. Adv. 2020, 2, 3115-30.

22. Jiang, Y. F.; Peng, H. Y.; Cai, Y.; et al. Adaptive ferroelectric memristors with high-throughput BaTiO₃ thin films for neuromorphic computing. Mater. Horiz. 2025, 12, 6928-37.

23. Koinuma, H.; Takeuchi, I. Combinatorial solid-state chemistry of inorganic materials. Nat. Mater. 2004, 3, 429-38.

24. Barber, Z. H.; Blamire, M. G. High throughput thin film materials science. Mater. Sci. Technol. 2008, 24, 757-70.

25. Yuan, J.; Stanev, V.; Gao, C.; Takeuchi, I.; Jin, K. Recent advances in high-throughput superconductivity research. Supercond. Sci. Technol. 2019, 32, 123001.

26. Wang, Z.; Sun, Z.; Yin, H.; et al. Data-driven materials innovation and applications. Adv. Mater. 2022, 34, e2104113.

27. Green, M. L.; Choi, C. L.; Hattrick-Simpers, J. R.; et al. Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies. Appl. Phys. Rev. 2017, 4, 011105.

28. Zhang, Y.; Tan, Y.; Dong, Y.; et al. High-throughput scanning second-harmonic-generation microscopy for polar materials. Adv. Mater. 2023, 35, e2300348.

29. Tang, M.; Dai, L.; Cheng, M.; et al. High‐throughput screening thickness‐dependent resistive switching in SrTiO₃ thin films for robust electronic synapse. Adv. Funct. Mater. 2023, 33, 2213874.

30. Fang, H.; Wang, J.; Nie, F.; et al. Giant electroresistance in ferroelectric tunnel junctions via high-throughput designs: toward high-performance neuromorphic computing. ACS. Appl. Mater. Interfaces. 2024, 16, 1015-24.

31. Zhang, D.; Harmon, K. J.; Zachman, M. J.; et al. High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system. InfoMat 2024, 6, e12561.

32. Xiao, R.; Zhang, Y.; Li, M. Automated high-throughput atomic force microscopy single-cell nanomechanical assay enabled by deep learning-based optical image recognition. Nano. Lett. 2024, 24, 12323-32.

33. Thomas-Chemin, O.; Janel, S.; Boumehdi, Z.; et al. Advancing high-throughput cellular atomic force microscopy with automation and artificial intelligence. ACS. Nano. 2025, 19, 5045-62.

34. Gregoire, J. M.; Van Campen, D. G.; Miller, C. E.; Jones, R. J.; Suram, S. K.; Mehta, A. High-throughput synchrotron X-ray diffraction for combinatorial phase mapping. J. Synchrotron. Radiat. 2014, 21, 1262-8.

35. Maruyama, S.; Ouchi, K.; Koganezawa, T.; Matsumoto, Y. High-throughput and autonomous grazing incidence X-ray diffraction mapping of organic combinatorial thin-film library driven by machine learning. ACS. Comb. Sci. 2020, 22, 348-55.

36. Ludwig, A. Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods. npj. Comput. Mater. 2019, 5, 205.

37. Kimmig, J.; Zechel, S.; Schubert, U. S. Digital transformation in materials science: a paradigm change in material’s development. Adv. Mater. 2021, 33, e2004940.

38. Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; Walsh, A. Machine learning for molecular and materials science. Nature 2018, 559, 547-55.

39. Sanchez-Lengeling, B.; Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 2018, 361, 360-5.

40. Himanen, L.; Geurts, A.; Foster, A. S.; Rinke, P. Data-driven materials science: status, challenges, and perspectives. Adv. Sci. 2019, 6, 1900808.

41. Hardian, R.; Liang, Z.; Zhang, X.; Szekely, G. Artificial intelligence: the silver bullet for sustainable materials development. Green. Chem. 2020, 22, 7521-8.

42. Xu, X.; Ma, W.; Yan, B. An electrodeposited nano-porous and neural network-like Ln@HOF film for SO₂ gas quantitative detection via fluorescent sensing and machine learning. J. Mater. Chem. A. 2021, 9, 26391-400.

43. Miseta, T.; Fodor, A.; Vathy-Fogarassy, Á. Surpassing early stopping: a novel correlation-based stopping criterion for neural networks. Neurocomputing 2024, 567, 127028.