Machine‐Learning‐Designed BCZT–SBT Heterointerface Unlocks Fatigue‐Resistant Energy Storage

Abstract

Abstract Dielectric capacitors are attractive for advanced energy storage owing to their ultrafast charge–discharge capability, yet their practical use is hindered by severe fatigue under repeated operation at ultrahigh electric fields. Achieving fatigue‐free performance therefore represents a key challenge in dielectric design. Here, guided by machine learning (ML), SrBi 2 Ta 2 O 9 (SBT) is introduced into Ba 0.85 Ca 0.15 Zr 0.1 Ti 0.9 O 3 (BCZT) to construct a (1‐ x )BCZT‐ x SBT solid solution. At x = 0.10, the coexistence of perovskite and tungsten bronze phases gives rise to an epitaxial interfacial layer only a few unit cells thick, formed by lattice mismatch. Atomic‐scale analyses reveal that this hetero‐barrier effectively suppresses carrier migration, while the tungsten bronze phase promotes polarization homogenization, together enhancing both voltage endurance and reliability. As a result, 0.90BCZT‐0.10SBT achieves a recoverable energy density ( W rec ) of 9.94 J cm −3 with 92.1% efficiency, and more strikingly, maintains stable performance after 10 9 charge–discharge cycles without degradation, enabled by an elevated Schottky barrier. This work not only uncovers the atomic origin of fatigue resistance in lead‐free dielectrics but also establishes a ML‐guided strategy for designing next‐generation high‐performance, fatigue‐free capacitors for reliable energy storage.