Designing Pb-Free High-Entropy Relaxor Ferroelectrics with Machine Learning Assistance for High Energy Storage

High-entropy tactics present exceptional promise in advancing the dielectric energy storage of relaxor ferroelectrics, thereby benefiting various pulsed-power electronic systems. However, their vast composition space poses challenges in the rational design of a high-performance system. Herein, we present a machine learning-supplemented strategy to design high-entropy relaxors, demonstrating an ultrahigh energy-storage density of 17.2 J cm –3 and high efficiency of 87% at a high breakdown strength of 79 kV mm –1 . By integrating six A -site and one B -site critical intrinsic features of constituent ions, deduced from a constructed random forest regression model, the (Bi 2/5 Na 1/5 K 1/5 Ba 1/5 )(Ti,Hf)O 3 high-entropy system is identified. Atomic-level local structural analysis reveals that incorporating these certified cations, with diverse local polar and lattice construction characteristics, results in a highly fluctuating local polarization structure. This favorable structure is characterized by pronounced orientation disorder and a broadly distributed length of unit-cell polarization vectors within the expanded lattice framework. Macroscopically, the optimized relaxor displays high dielectric susceptibility and large resistance. Moreover, a large discharge energy density of 5.8 J cm –3 and power energy density of 447 MW cm –3, along with outstanding operational stability, are achieved. This study presents a data-driven model to explore complex intrinsic features and facilitate the design of high-performance relaxors.