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High-temperature polymer composite capacitors with high energy density designed via machine learning

Author

Listed:
  • Minzheng Yang

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Chaofan Wan

    (Wuhan University of Technology, State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices)

  • Le Zhou

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Xiao Li

    (Tsinghua University, Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry)

  • Jiayu Pan

    (Wuzhen Laboratory, Research Center for New Functional Composites)

  • Haoyang Li

    (Tsinghua University, Department of Chemistry, Engineering Research Center of Advanced Rare Earth Materials)

  • Jian Wang

    (Wuhan University of Technology, State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices)

  • Weibin Ren

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Binzhou Sun

    (Wuzhen Laboratory, Research Center for New Functional Composites)

  • Erxiang Xu

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Yao Xiao

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Mengfan Guo

    (University of Cambridge, Department of Materials Science)

  • Mufeng Zhang

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Xin Li

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Jianyong Jiang

    (Wuzhen Laboratory, Research Center for New Functional Composites)

  • Penghao Hu

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Lian Duan

    (Tsinghua University, Key Lab of Organic Optoelectronics and Molecular Engineering of Ministry of Education, Department of Chemistry
    Tsinghua University, Laboratory of Flexible Electronics Technology)

  • Ce-Wen Nan

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

  • Zhonghui Shen

    (Wuhan University of Technology, State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Center of Smart Materials and Devices)

  • Xun Wang

    (Tsinghua University, Department of Chemistry, Engineering Research Center of Advanced Rare Earth Materials)

  • Yang Shen

    (Tsinghua University, State Key Laboratory of New Ceramic Materials, School of Materials Science and Engineering)

Abstract

Polymer dielectrics are the primary energy storage media in electrostatic capacitors, which are essential components in power electronics for electric vehicles and renewable energy systems. Composite approach has been intensively explored to enhance the energy density (Ud) and breakdown strength (Eb) of polymers at high temperatures, but finding fillers with both a large bandgap (Eg) and high electronic affinity (Ea) remains challenging. Here, assisted by a generative machine learning approach, we discover and synthesize organic fillers of both a large Eg (~5.5 eV) and high Ea (~4.5 eV). These fillers enable polyimide composite films to deliver a Ud of 5.1 J cm−3 at discharge efficiency of 90% and 2 × 105 charge–discharge cycles at 250 °C. Moreover, we fabricate high-quality, kilometre-scale composite films using roll-to-roll processing and demonstrate that industrial capacitors incorporating these metalized composite films exhibit stable discharge and self healing in harsh environments.

Suggested Citation

  • Minzheng Yang & Chaofan Wan & Le Zhou & Xiao Li & Jiayu Pan & Haoyang Li & Jian Wang & Weibin Ren & Binzhou Sun & Erxiang Xu & Yao Xiao & Mengfan Guo & Mufeng Zhang & Xin Li & Jianyong Jiang & Penghao, 2025. "High-temperature polymer composite capacitors with high energy density designed via machine learning," Nature Energy, Nature, vol. 10(11), pages 1323-1333, November.
    • Handle: RePEc:nat:natene:v:10:y:2025:i:11:d:10.1038_s41560-025-01863-0
    DOI: 10.1038/s41560-025-01863-0
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