Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61794-y |
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| author | 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 |
| author_facet | 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 |
| author_sort | Jing Sun |
| collection | DOAJ |
| description | 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). |
| format | Article |
| id | doaj-art-9ea00e3bbcd34bb598e2853bd62d3e7f |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9ea00e3bbcd34bb598e2853bd62d3e7f2025-08-20T03:43:22ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-61794-yArtificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learningJing Sun0Pengzhu Lin1Lin Zeng2Zixiao Guo3Yuting Jiang4Cailin Xiao5Qinping Jian6Jiayou Ren7Lyuming Pan8Xiaosa Xu9Zheng Li10Lei Wei11Tianshou Zhao12Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Energy Engineering, Southern University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Energy Engineering, Southern University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Energy Engineering, Southern University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyDepartment of Mechanical and Energy Engineering, Southern University of Science and TechnologyDepartment of Mechanical and Aerospace Engineering, The Hong Kong University of Science and TechnologyAbstract 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).https://doi.org/10.1038/s41467-025-61794-y |
| spellingShingle | 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 Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning Nature Communications |
| title | Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning |
| title_full | Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning |
| title_fullStr | Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning |
| title_full_unstemmed | Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning |
| title_short | Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via Bayesian machine learning |
| title_sort | artificial intelligence guided design of ordered gas diffusion layers for high performing fuel cells via bayesian machine learning |
| url | https://doi.org/10.1038/s41467-025-61794-y |
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