Programming Polarity Heterogeneity of Energy Storage Dielectrics by Bidirectional Intelligent Design

Dielectric capacitors, characterized by ultra-high power densities, are considered as fundamental energy storage components in electronic and electrical systems. However, synergistically improving energy densities and efficiencies remains a daunting challenge. Understanding the role of local polar heterogeneity at the nanoscale in determining polarization response is crucial to the domain engineering of high-performance dielectrics. Here, a bidirectional design with phase-field simulation and machine learning are performed to forward reveal the structure-property relationship and reversely optimize polarity heterogeneity to improve energy storage performance. Taking BiFeO3 -based dielectrics as typical systems, we establish the mapping diagrams of energy density and efficiency dependence on the volume fraction, size and configuration of polar regions. Assisted by CatBoost and Wolf Pack algorithms, we analyze the contributions of geometric factors and intrinsic features to energy storage performance and find that nanopillar-like polar regions show great potential in achieving both high polarization intensity and fast dipole switching. Finally, a maximal energy density of 188 J cm-3 with efficiency above 95% at an applied electric field of 8 MV cm-1 is obtained in BiFeO3 -Al2 O3 systems. This work provides a general method to study the influence of local polar heterogeneity in multiphase dielectrics on polarization behaviors and proposes effective strategies to enhance energy storage performance by tuning local polarity heterogeneity. This article is protected by copyright. All rights reserved.