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This article discusses the use of generative learning models to accelerate the discovery of high-entropy dielectrics (HEDs) with high energy density. The authors use phase field simulations and experimental data to design HEDs with optimal combinations of elements, resulting in a greatly improved energy storage performance. This approach can be extended to the design of other high-entropy materials.
