Machine Learning-Guided Electronic Configuration Design for Dielectrics With High Energy Storage Performance

Dielectric capacitors possess ultrahigh power density and are promising for advanced energy storage and pulsed power applications, yet conventional trial-and-error doping strategies limit design efficiency and performance optimization. Here, an interpretable machine learning framework based on Shapley Additive exPlanations is developed to guide the compositional design of K_0.5Na_0.5NbO₃-based relaxor ferroelectrics. Key descriptors governing polarization behavior are identified from 18 features, leading to Sc as the optimal dopant, outperforming the Bi_5/6In_0.5Sn_0.5O₃-doped system. The designed 0.85(0.96K_0.48Na_0.52NbO₃-0.04BaZrO₃)-0.15Bi_5/6Sc_0.5Sn_0.5O₃ is predicted to exhibit a large polarization difference of 26.3 µC cm^-2. First-principles calculations and experimental validation show that Sc³⁺ forms more ionic Sc-O bond than In³⁺, giving less distorted and markedly more rigid polar units. These rigid units act as a restoring force during polarization switching and suppress the remnant polarization, while the strong Sc─O bond reduces oxygen vacancy concentration and enhances the dielectric breakdown strength. As a result, an ultrahigh energy storage density of 6.91 J cm^-3 and efficiency of 91.9% are achieved, representing a 66.1% improvement over the In-based system. This work demonstrates an interpretable machine learning-guided electronic configuration strategy and highlights the critical role of dopant electronic structure and local bonding in optimizing energy storage performance.