Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics
Source abstract
Abstract
Atomistic control of phase boundaries is crucial for optimizing the functional properties of solid-solution ferroelectrics, yet their microstructural mechanisms remain elusive. Here, we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO 3 –KTaO 3 (KNTO) system. Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries (PPBs), offering a versatile pathway for materials engineering. Diffused PPBs and polar nano regions, predicted by our model, highly match with experiments, underscoring the fidelity of the machine-learning atomistic simulation. Crucially, we identify elastic and electrostatic mismatches between ferroelectric KNbO 3 and paraelectric KTaO 3 as the driving forces behind complex microstructural evolution. This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Ferroelectric and Piezoelectric Materials · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (64)
Show source-provided references
1. Zhang, M. et al. Ultrahigh energy storage in high-entropy ceramic capacitors with polymorphic relaxor phase. Science 384, 185–189 (2024).10.1126/science.adl2931 ↗
2. Xu, Z. et al. High electrostrain in a lead-free piezoceramic from a chemopiezoelectric effect. Nat. Mater. 24, 565–573 (2025).10.1038/s41563-024-02092-8 ↗
3. Yasuda, K. et al. Ultrafast high-endurance memory based on sliding ferroelectrics. Science 385, 53–56 (2024).10.1126/science.adp3575 ↗
4. Wang, T. et al. Large enhancement of ferroelectric properties of perovskite oxides via nitrogen incorporation. Sci. Adv. 11, eads8830 (2025).10.1126/sciadv.ads8830 ↗
5. Du, W. et al. Conformable ultrasound breast patch for deep tissue scanning and imaging. Sci. Adv. 9, eadh5325 (2023).10.1126/sciadv.adh5325 ↗
6. Aramberri, H., Fedorova, N. S. & Íñiguez, J. J. S. A. Ferroelectric/paraelectric superlattices for energy storage. Sci. Adv. 8, eabn4880 (2022).10.1126/sciadv.abn4880 ↗
7. Chuang, C.-H. et al. Sharp transformation across morphotropic phase boundary in sub-6 nm wake-up-free ferroelectric films by atomic layer technology. Adv. Sci. 10, 2302770 (2023).10.1002/advs.202302770 ↗
8. Xu, L. et al. Ultrahigh thermal stability and piezoelectricity of lead-free KNN-based texture piezoceramics. Nat. Commun. 15, 9018 (2024).10.1038/s41467-024-53437-5 ↗
9. Damjanovic, D. A morphotropic phase boundary system based on polarization rotation and polarization extension. Appl. Phys. Lett. 97, 062906 (2010).10.1063/1.3479479 ↗
10. Li, J. et al. Lead zirconate titanate ceramics with aligned crystallite grains. Science 380, 87–93 (2023).10.1126/science.adf6161 ↗
11. Jo, W. et al. Evolving morphotropic phase boundary in lead-free (Bi1/2Na1/2)TiO3–BaTiO3 piezoceramics. J. Appl. Phys. 109, 014110 (2011).10.1063/1.3530737 ↗
12. Li, F. et al. Composition and phase dependence of the intrinsic and extrinsic piezoelectric activity of domain engineered (1−x)Pb(Mg1/3Nb2/3)O3−xPbTiO3 crystals. J. Appl. Phys. 108, 034106 (2010).10.1063/1.3466978 ↗
13. Li, F. et al. Giant piezoelectricity of Sm-doped Pb(Mg1/3Nb2/3)O3-PbTiO3 single crystals. Science 364, 264–268 (2019).10.1126/science.aaw2781 ↗
14. Du, F. et al. Giant electrocaloric effect in high-polar-entropy perovskite oxides. Nature 640, 924–930 (2025).10.1038/s41586-025-08768-8 ↗
15. Qian, X.-S. et al. Giant electrocaloric response over a broad temperature range in modified BaTiO3 ceramics. Adv. Funct. Mater. 24, 1300–1305 (2014).10.1002/adfm.201302386 ↗
16. Liu, W. et al. Ultrahigh piezoelectricity and temperature stability in piezoceramics by synergistic design. Nat. Commun. 16, 1527 (2025).10.1038/s41467-025-56798-7 ↗
17. Hua, Y. et al. Broad temperature plateau for high piezoelectric coefficient by embedding PNRs in singe-phase KNN-based ceramics. Adv. Funct. Mater. 35, 2414348 (2025).10.1002/adfm.202414348 ↗
18. Cao, W. W. & Cross, L. E. Theoretical-model for the morphotropic phase-boundary in lead zirconate lead titanate solid-solution. Phys. Rev. B 47, 4825–4830 (1993).10.1103/PhysRevB.47.4825 ↗
19. Noheda, B. et al. Tetragonal-to-monoclinic phase transition in a ferroelectric perovskite: the structure of PbZr0.52Ti0.48O3. Phys. Rev. B 61, 8687–8695 (2000).10.1103/PhysRevB.61.8687 ↗
20. Zhong, W., Vanderbilt, D. & Rabe, K. M. First-principles theory of ferroelectric phase transitions for perovskites: The case of BaTiO3. Phys. Rev. B 52, 6301–6312 (1995).10.1103/PhysRevB.52.6301 ↗
21. Nahas, Y. et al. Microscopic origins of the large piezoelectricity of leadfree (Ba,Ca)(Zr,Ti)O3. Nat. Commun. 8, 15944 (2017).10.1038/ncomms15944 ↗
22. Ghosez, P. & Junquera, J. Modeling of ferroelectric oxide perovskites: from first to second principles. Annu. Rev. Condens. Matter Phys. 13, 325–364 (2022).10.1146/annurev-conmatphys-040220-045528 ↗
23. Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).10.1103/PhysRevLett.104.136403 ↗
24. Chmiela, S. et al. Accurate global machine learning force fields for molecules with hundreds of atoms. Sci. Adv. 9, eadf0873 (2023).10.1126/sciadv.adf0873 ↗
25. Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).10.1038/s41467-022-29939-5 ↗
26. Batatia, I. K. et al. Higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neural Inf. Process. Syst. 35, 11423–11436 (2022).
27. Deng, B. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mach. Intell. 5, 1031–1041 (2023).10.1038/s42256-023-00716-3 ↗
28. Behler, J. & Parrinello, M. J. P. r. l. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).10.1103/PhysRevLett.98.146401 ↗
29. Zhang, L. et al. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).10.1103/PhysRevLett.120.143001 ↗
30. Zhang, H. et al. Finite-temperature properties of the antiferroelectric perovskite PbZrO3 from a deep-learning interatomic potential. Phys. Rev. B 110, 054109 (2024).10.1103/PhysRevB.110.054109 ↗
31. Ma, L. et al. Ultrahigh oxygen ion mobility in ferroelectric hafnia. Phys. Rev. Lett. 131, 256801 (2023).10.1103/PhysRevLett.131.256801 ↗
32. Gao, Y., Deng, F., He, R. & Zhong, Z. Spontaneous curvature in two-dimensional van der Waals heterostructures. Nat. Commun. 16, 717 (2025).10.1038/s41467-025-56055-x ↗
33. Wu, J. et al. Universal interatomic potential for perovskite oxides. Phys. Rev. B 108, L180104 (2023).10.1103/PhysRevB.108.L180104 ↗
34. Wang, X. et al. Potassium tantalate niobate crystals: efficient quadratic electro-optic materials and their laser modulation technology. J. Mater. 9, 838–854 (2023).
35. Di Mei, F. et al. Giant broadband refraction in the visible in a ferroelectric perovskite. Nat. Photonics 12, 734–738 (2018).10.1038/s41566-018-0276-3 ↗
36. Tan, P. et al. Deciphering the atomistic mechanism underlying highly tunable piezoelectric properties in perovskite ferroelectrics via transition metal doping. Nat. Commun. 15, 1–12 (2024).10.1038/s41467-024-54842-6 ↗
37. Chang, Y.-C., Wang, C., Yin, S., Hoffman, R. C. & Mott, A. G. Giant electro-optic effect in nanodisordered KTN crystals. Opt. Lett. 38, 4574–4577 (2013).10.1364/OL.38.004574 ↗
38. Shogo, Y. KTN crystals open up new possibilities and applications. NTT Tech. Rev. 7, 16–20 (2009).10.53829/ntr200912sf3 ↗
39. Parravicini, J. et al. Key role of polar nanoregions in the cubic-to-tetragonal phase transition of potassium-based perovskites. Phys. Rev. B 106, 064107 (2022).10.1103/PhysRevB.106.064107 ↗
40. Rahaman, M. M. et al. Relaxor-like dynamics of ferroelectric K(Ta1-xNbx)O3 crystals probed by inelastic light scattering. J. Appl. Phys. 116, 6 (2014).10.1063/1.4893363 ↗
41. Tan, P. et al. Temperature field driven polar nanoregions in KTa1−xNbxO3. Appl. Phys. Lett. 109, 252904 (2016).10.1063/1.4972783 ↗
42. Pierangeli, D. et al. Super-crystals in composite ferroelectrics. Nat. Commun. 7, 10674 (2016).10.1038/ncomms10674 ↗
43. Lv, X., Zhu, J., Xiao, D., Zhang, X. -x & Wu, J. Emerging new phase boundary in potassium sodium-niobate based ceramics. Chem. Soc. Rev. 49, 671–707 (2020).10.1039/C9CS00432G ↗
44. Phillpot, S. R., Sepliarsky, M., Stachiotti, M. G., Migoni, R. L. & Streiffer, S. K. Order-disorder behavior in KNbO3 and KNbO3/KTaO3 solid solutions and superlattices by molecular-dynamics simulation. J. Mater. Sci. 40, 3213–3217 (2005).10.1007/s10853-005-2687-z ↗
45. Thong, H.-C. et al. Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite. Phys. Rev. B 107, 014101 (2023).10.1103/PhysRevB.107.014101 ↗
46. He, X. et al. Resolving the dynamic correlated disorder in KTa1-xNbxO3. Proc. Natl. Acad. Sci. USA 122, e2419159122 (2025).10.1073/pnas.2419159122 ↗
47. Triebwasser, S. Study of ferroelectric transitions of solid-solution single crystals of KNbO3-KTaO3. Phys. Rev. 114, 63–70 (1959).10.1103/PhysRev.114.63 ↗
48. Zheng, T. et al. Compositionally graded KNN-based multilayer composite with excellent piezoelectric temperature stability. Adv. Mater. 34, 2109175 (2022).10.1002/adma.202109175 ↗
49. Toulouse, J. & Pattnaik, R. Relaxor and superparaelectric behavior in the disordered ferroelectrics KLT and KTN. Ferroelectrics 199, 287–305 (1997).10.1080/00150199708213451 ↗
50. Venkatesh, J., Sherman, V. & Setter, N. Synthesis and dielectric characterization of potassium niobate tantalate ceramics. J. Am. Ceram. Soc. 88, 3397–3404 (2005).10.1111/j.1551-2916.2005.00606.x ↗
51. Thong, H.-C. et al. The origin of chemical inhomogeneity in lead-free potassium sodium niobate ceramic: competitive chemical reaction during solid-state synthesis. Acta Mater. 211, 116833 (2021).10.1016/j.actamat.2021.116833 ↗
52. Thong, H.-C. et al. Domain engineering in bulk ferroelectric ceramics via mesoscopic chemical inhomogeneity. Adv. Sci. 9, 2200998 (2022).10.1002/advs.202200998 ↗
53. Dove, M. T. Introduction to lattice dynamics (Cambridge University Press, 1993).10.1017/CBO9780511619885 ↗
54. Viehland, D., Wuttig, M. & Cross, L. E. The glassy behavior of relaxor ferroelectrics. Ferroelectrics 120, 71–77 (1991).10.1080/00150199108216802 ↗
55. Akbarzadeh, A. R., Prosandeev, S., Walter, E. J., Al-Barakaty, A. & Bellaiche, L. Finite-temperature properties of Ba(Zr,Ti)O3 relaxors from first principles. Phys. Rev. Lett. 108, 257601 (2012).10.1103/PhysRevLett.108.257601 ↗
56. Zhang, Y. et al. DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 253, 107206 (2020).10.1016/j.cpc.2020.107206 ↗
57. Wang, H., Zhang, L., Han, J. & Weinan, E. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 228, 178–184 (2018).10.1016/j.cpc.2018.03.016 ↗
58. Han, J. Q., Zhang, L. F., Car, R. & Weinan, E. Deep potential: a general representation of a many-body potential energy surface. Commun. Comput. Phys. 23, 629–639 (2018).10.4208/cicp.OA-2017-0213 ↗
59. Thompson, A. P. et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022).10.1016/j.cpc.2021.108171 ↗
60. Hirel, P. Atomsk: a tool for manipulating and converting atomic data files. Comput. Phys. Commun. 197, 212–219 (2015).10.1016/j.cpc.2015.07.012 ↗
61. Zhong, W., King-Smith, R. D. & Vanderbilt, D. Giant LO-TO splittings in perovskite ferroelectrics. Phys. Rev. Lett. 72, 3618–3621 (1994).10.1103/PhysRevLett.72.3618 ↗
62. Momma, K. & Izumi, F. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr. 44, 1272–1276 (2011).10.1107/S0021889811038970 ↗
63. Ahrens, J., Geveci, B. & Law, C. ParaView: An End-User Tool for Large Data Visualization. The Visualization Handbook 8, 717–731 (2005).10.1016/B978-012387582-2/50038-1 ↗
64. Togo, A., Chaput, L., Tadano, T. & Tanaka, I. Implementation strategies in phonopy and phono3py. J. Phys. Condens. Matter 35, 353001 (2023).10.1088/1361-648X/acd831 ↗