Machine‐Learning‐Guided Polarization‐Lattice Decoupling Enables Ultrahigh Energy Storage in Lead‐Free Dielectric Ceramics

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 ₃ ‐based dielectric matrices, enabling the rational identification of compositions with intrinsically high polarization potential. Guided by this strategy, a (Bi _0.275 Na _0.2255 K _0.0495 Ba _0.3 )(Ti _0.985 Hf _0.015 )O ₃ ‐0.15(La _0.5 Sm _0.5 ) ₂ Ti ₂ O ₇ (BNBT‐3) composition is discovered that exhibits an exceptional maximum polarization of 50.19 µC cm ⁻² . When processed via a viscous polymer process, the resulting BNBT‐3‐VPP capacitors achieve an ultrahigh breakdown strength of 1400 kV cm ⁻¹ and a recoverable energy density of 25.1 J cm ⁻³ 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.