Machine-Learning-Designed BCZT-SBT Heterointerface Unlocks Fatigue-Resistant Energy Storage.
basic_science · Level V
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- Record sourced from PubMed, PMID 41347849.
- Also identified by DOI 10.1002/adma.202519635.
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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<sub>2</sub>Ta<sub>2</sub>O<sub>9</sub> (SBT) is introduced into Ba<sub>0.85</sub>Ca<sub>0.15</sub>Zr<sub>0.1</sub>Ti<sub>0.9</sub>O<sub>3</sub> (BCZT) to construct a (1-x)BCZT-xSBT 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<sub>rec</sub>) of 9.94 J cm<sup>-3</sup> with 92.1% efficiency, and more strikingly, maintains stable performance after 10<sup>9</sup> 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.