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Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics

Author

Listed:
  • Zhong-Hui Shen

    (Tsinghua University)

  • Jian-Jun Wang

    (The Pennsylvania State University)

  • Jian-Yong Jiang

    (Tsinghua University)

  • Sharon X. Huang

    (The Pennsylvania State University)

  • Yuan-Hua Lin

    (Tsinghua University)

  • Ce-Wen Nan

    (Tsinghua University)

  • Long-Qing Chen

    (The Pennsylvania State University)

  • Yang Shen

    (Tsinghua University
    Tsinghua University)

Abstract

Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.

Suggested Citation

  • Zhong-Hui Shen & Jian-Jun Wang & Jian-Yong Jiang & Sharon X. Huang & Yuan-Hua Lin & Ce-Wen Nan & Long-Qing Chen & Yang Shen, 2019. "Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09874-8
    DOI: 10.1038/s41467-019-09874-8
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    Cited by:

    1. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.

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