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Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage

Author

Listed:
  • Xingcheng Wang

    (University of Science and Technology Beijing)

  • Ji Zhang

    (Nanjing University of Science and Technology)

  • Xingshuai Ma

    (University of Science and Technology Beijing)

  • Huajie Luo

    (University of Science and Technology Beijing)

  • Laijun Liu

    (Guilin University of Technology)

  • Hui Liu

    (University of Science and Technology Beijing)

  • Jun Chen

    (University of Science and Technology Beijing)

Abstract

The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi0.5Na0.5)TiO3-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown strength of 95 kV mm-1. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm-3 under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors.

Suggested Citation

  • Xingcheng Wang & Ji Zhang & Xingshuai Ma & Huajie Luo & Laijun Liu & Hui Liu & Jun Chen, 2025. "Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage," Nature Communications, Nature, vol. 16(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-56443-3
    DOI: 10.1038/s41467-025-56443-3
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    as
    1. Haonan Peng & Tiantian Wu & Zhen Liu & Zhengqian Fu & Dong Wang & Yanshuang Hao & Fangfang Xu & Genshui Wang & Junhao Chu, 2024. "High-entropy relaxor ferroelectric ceramics for ultrahigh energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. 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 & Ce-Wen Nan, 2024. "Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Bingbing Yang & Qinghua Zhang & Houbing Huang & Hao Pan & Wenxuan Zhu & Fanqi Meng & Shun Lan & Yiqian Liu & Bin Wei & Yiqun Liu & Letao Yang & Lin Gu & Long-Qing Chen & Ce-Wen Nan & Yuan-Hua Lin, 2023. "Engineering relaxors by entropy for high energy storage performance," Nature Energy, Nature, vol. 8(9), pages 956-964, September.
    4. Liang Chen & Shiqing Deng & Hui Liu & Jie Wu & He Qi & Jun Chen, 2022. "Giant energy-storage density with ultrahigh efficiency in lead-free relaxors via high-entropy design," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    5. M. Eremenko & V. Krayzman & A. Bosak & H. Y. Playford & K. W. Chapman & J. C. Woicik & B. Ravel & I. Levin, 2019. "Local atomic order and hierarchical polar nanoregions in a classical relaxor ferroelectric," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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