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Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning

Author

Listed:
  • Jing Sun

    (The Hong Kong University of Science and Technology)

  • Pengzhu Lin

    (The Hong Kong University of Science and Technology)

  • Lin Zeng

    (Southern University of Science and Technology)

  • Zixiao Guo

    (The Hong Kong University of Science and Technology)

  • Yuting Jiang

    (The Hong Kong University of Science and Technology
    Southern University of Science and Technology)

  • Cailin Xiao

    (Southern University of Science and Technology)

  • Qinping Jian

    (The Hong Kong University of Science and Technology)

  • Jiayou Ren

    (The Hong Kong University of Science and Technology)

  • Lyuming Pan

    (Southern University of Science and Technology)

  • Xiaosa Xu

    (The Hong Kong University of Science and Technology)

  • Zheng Li

    (The Hong Kong University of Science and Technology
    Southern University of Science and Technology)

  • Lei Wei

    (Southern University of Science and Technology)

  • Tianshou Zhao

    (The Hong Kong University of Science and Technology
    Southern University of Science and Technology)

Abstract

Rational design of gas diffusion layers (GDL) is an example of a long-standing pursuit to increase the power density and reduce the cost of proton exchange membrane fuel cells (PEMFC). However, current state-of-the-art GDLs are designed by trial-and-error, which is a time-consuming endeavor. Here, we propose a closed-loop workflow of Bayesian machine learning approach to guide the design of GDL structures. With artificial neural network accelerating the calculation of anisotropic transport properties of reconstructed GDLs, Bayesian optimization algorithm identifies optimal structures in only 40 steps, maximizing the PEMFC’s limiting current density. Results suggest that the optimal porous GDL structure consists of highly orientated fibers with moderate diameters, which is successfully fabricated with a controlled electrospinning technique. The PEMFC demonstrates a high power density of 2.17 W cm-2 and a limiting current density of ~7200 mA cm-2, far exceeding that with commercial GDL (1.33 W cm-2 and ~2700 mA cm-2).

Suggested Citation

  • Jing Sun & Pengzhu Lin & Lin Zeng & Zixiao Guo & Yuting Jiang & Cailin Xiao & Qinping Jian & Jiayou Ren & Lyuming Pan & Xiaosa Xu & Zheng Li & Lei Wei & Tianshou Zhao, 2025. "Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61794-y
    DOI: 10.1038/s41467-025-61794-y
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    References listed on IDEAS

    as
    1. Zhisen Jiang & Jizhou Li & Yang Yang & Linqin Mu & Chenxi Wei & Xiqian Yu & Piero Pianetta & Kejie Zhao & Peter Cloetens & Feng Lin & Yijin Liu, 2020. "Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    2. David A. Cullen & K. C. Neyerlin & Rajesh K. Ahluwalia & Rangachary Mukundan & Karren L. More & Rodney L. Borup & Adam Z. Weber & Deborah J. Myers & Ahmet Kusoglu, 2021. "New roads and challenges for fuel cells in heavy-duty transportation," Nature Energy, Nature, vol. 6(5), pages 462-474, May.
    3. Zhang, Qian & Lin, Rui & Técher, Ludovic & Cui, Xin, 2016. "Experimental study of variable operating parameters effects on overall PEMFC performance and spatial performance distribution," Energy, Elsevier, vol. 115(P1), pages 550-560.
    4. ChungHyuk Lee & Wilton J. M. Kort-Kamp & Haoran Yu & David A. Cullen & Brian M. Patterson & Tanvir Alam Arman & Siddharth Komini Babu & Rangachary Mukundan & Rod L. Borup & Jacob S. Spendelow, 2023. "Grooved electrodes for high-power-density fuel cells," Nature Energy, Nature, vol. 8(7), pages 685-694, July.
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