Atomic‐Scale Intermediate Polarization States Enable Superb Energy Storage in NaNbO 3 Ceramics via Machine Learning

ABSTRACT Dielectric energy‑storage ceramics face a fundamental performance limitation stemming from the intrinsic trade‑off between achieving large polarization and minimizing hysteresis loss. This challenge is particularly pronounced in lead‑free NaNbO 3 ‑based materials, where high electric fields induce irreversible antiferroelectric‐ferroelectric phase transitions. To overcome this limitation, we develop a machine‑learning‑assisted design strategy that guides the creation of atomic‑scale intermediate polarization states (IPSs) within dual‑phase NaNbO 3 heterostructures. Aberration‑corrected scanning transmission electron microscopy directly visualizes IPSs between the tetragonal (T‐phase) and rhombohedral (R‐phase), which reduce polarization anisotropy and flatten the free‑energy landscape, thereby enabling concurrent large polarization and minimal hysteresis. The optimized ceramic delivers a high recoverable energy density of 10.24 J cm −3 with an exceptional efficiency of 92% under 920 kV cm −1 . This work establishes a materials‑design paradigm that decouples polarization from energy loss through the integration of atomic‐scale structural control and machine learning, providing a promising pathway toward advanced dielectrics for high‐power energy‐storage applications.