Machine-Learning-Guided Polarization-Lattice Decoupling Enables Ultrahigh Energy Storage in Lead-Free Dielectric Ceramics.
basic_science · Level V
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- Record sourced from PubMed, PMID 42555245.
- Also identified by DOI 10.1002/adma.73801.
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Abstract
Achieving ultrahigh energy storage in lead-free dielectric ceramics is fundamentally constrained by the intrinsic trade-off between large polarization and high dielectric breakdown strength. Here, we establish an interpretable machine-learning-guided design framework that quantitatively links ionic descriptors with polarization behavior in ABO<sub>3</sub>-based dielectric matrices, enabling the rational identification of compositions with intrinsically high polarization potential. Guided by this strategy, a (Bi<sub>0.275</sub>Na<sub>0.2255</sub>K<sub>0.0495</sub>Ba<sub>0.3</sub>)(Ti<sub>0.985</sub>Hf<sub>0.015</sub>)O<sub>3</sub>-0.15(La<sub>0.5</sub>Sm<sub>0.5</sub>)<sub>2</sub>Ti<sub>2</sub>O<sub>7</sub> (BNBT-3) composition is discovered that exhibits an exceptional maximum polarization of 50.19 µC cm<sup>-2</sup>. When processed via a viscous polymer process, the resulting BNBT-3-VPP capacitors achieve an ultrahigh breakdown strength of 1400 kV cm<sup>-1</sup> and a recoverable energy density of 25.1 J cm<sup>-3</sup> with high efficiency, placing them among the best-performing lead-free dielectric ceramics reported to date. Structural characterization combined with phase-field simulations reveals that the outstanding performance originates from polarization-lattice decoupling, where nanoscale polarization clusters and multiphase coexistence suppress long-range ferroelectric order while enabling reversible polarization rotation. This work establishes a generalizable strategy that integrates interpretable machine learning with physically grounded materials design, providing a powerful route for discovering high-performance dielectric energy storage materials.