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

Wei Li, Zhong-Hui Shen (), Run-Lin Liu, Xiao-Xiao Chen, Meng-Fan Guo, Jin-Ming Guo, Hua Hao, Yang Shen, Han-Xing Liu, Long-Qing Chen and Ce-Wen Nan ()
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Wei Li: Wuhan University of Technology
Zhong-Hui Shen: Wuhan University of Technology
Run-Lin Liu: Wuhan University of Technology
Xiao-Xiao Chen: Wuhan University of Technology
Meng-Fan Guo: Tsinghua University
Jin-Ming Guo: Hubei University
Hua Hao: Wuhan University of Technology
Yang Shen: The Pennsylvania State University
Han-Xing Liu: Wuhan University of Technology
Long-Qing Chen: The Pennsylvania State University
Ce-Wen Nan: Tsinghua University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract 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(Mg0.5Ti0.5)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.

Date: 2024
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DOI: 10.1038/s41467-024-49170-8

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