Machine learning-driven BaTiO_(3)-based high-entropy ceramics with ultrahigh energy storage density from crossover regionOA
The high-entropy strategy has demonstrated significant advantages in improving the recoverable energy storage density(W_(rec))and efficiency(η)of lead-free dielectric capacitors.However,exploring high-performance ceramics within the vast composition space of high-entropy systems using traditional trial-and-error methods remains highly challenging and inefficient.In this study,we employed a machine learning(ML)-accelerated strategy to overcome this limitation.A random forest regression model was developed using a dataset of BaTiO_(3)(BT)-based ceramics.Combined with the expected improvement acquisition function,this approach enabled efficient navigation through a space of 660,000 candidate compositions,markedly reducing the experimental burden compared with conventional methods.The optimal composition guided by ML,Ba_(0.24)Sr_(0.24)Bi_(0.26)Na_(0.26)Ti_(0.85)Zr_(0.15)O_(3),was experimentally verified to lie in the crossover region between relaxor ferroelectrics and superparaelectrics.In this region,the synergistic coexistence of nanodomains and polar nanoclusters leads to a large polarization difference between the maximum polarization and the remnant polarization(ΔP=P_(max)-P_(r)),which is the structural origin of the ultrahigh W_(rec) of 10.8 J·cm^(-3) and high η of 86%.Furthermore,its excellent charge-discharge performance and stability in terms of temperature and frequency highlight its potential for practical applications,demonstrating the efficacy of machine learning in advancing energy storage ceramics.
Haowen Liu;Xiaoyan Zhang;Zhiyuan Ma;Kailong Ma;Xiwei Qi
School of Materials Science and Engineering,Northeastern University,Shenyang 110819,China Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province,Northeastern University at Qinhuangdao,Qinhuangdao 066004,ChinaSchool of Materials Science and Engineering,Northeastern University,Shenyang 110819,China School of Resources and Materials,Northeastern University at Qinhuangdao,Qinhuangdao 066004,China Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province,Northeastern University at Qinhuangdao,Qinhuangdao 066004,ChinaSchool of Materials Science and Engineering,Northeastern University,Shenyang 110819,China Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province,Northeastern University at Qinhuangdao,Qinhuangdao 066004,ChinaSchool of Materials Science and Engineering,Northeastern University,Shenyang 110819,China Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province,Northeastern University at Qinhuangdao,Qinhuangdao 066004,ChinaSchool of Material Science and Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
化学化工
machine learninghigh entropyenergy storage capacitorcrossover region
《Journal of Advanced Ceramics》 2026 (4)
P.243-250,8
supported by the National Natural Science Foundation of China(Nos.U23A20605 and 52572011)Performance subsidy fund for Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province(No.22567627H)Hebei Provincial Department of Education Funding Project for Cultivating Innovation Capacity of Postgraduate Students(No.CXZZBS2025207).
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