Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage.

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 10¹¹ combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg_0.5Ti_0.5)O₃-based high-entropy dielectric film with a significantly improved energy density of 156 J cm⁻³ at an electric field of 5104 kV cm⁻¹, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties. High-entropy ceramic dielectrics show promise for capacitive energy storage but struggle due to vast composition possibilities. Here, the authors propose a generative learning approach for finding high-energy-density high-entropy dielectrics in a practically infinite exploration space of over 10¹¹ combinations. [ABSTRACT FROM AUTHOR]