How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

The development of efficient and stable hydrogen production technologies is crucial for global clean energy transition. Solid oxide electrolysis cells (SOECs) have emerged as a promising technology for green hydrogen production due to their high efficiency, low-cost catalysts, and excellent adaptability to renewable energy sources. However, significant challenges remain in materials design, interface engineering, and system integration. This perspective reviews recent advances in artificial intelligence (AI)-guided SOEC development, focusing on machine learning approaches for design of key materials. Furthermore, we highlight how AI technologies can address the key challenges in both single-cell performances and system-level integration with renewable energy sources. Looking forward, we outline strategic directions for advancing AI-driven SOEC development toward commercial implementation, which may offer valuable insights and experiences within the field of energy conversion and storage.

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Publication Details

Journal
Journal of Materials Informatics
Published
2025-03-22
DOI
https://doi.org/10.20517/jmi.2024.106
Citations
7
Primary Topic
Advancements in Solid Oxide Fuel Cells
Type
article
Field-Weighted Citation Impact
3.62
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article

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Siyu Ye, Tang Chun-mei, Baoyin Yuan, Ning Wang et al.
7 citations
Journal of Materials Informatics
Advancements in Solid Oxide Fuel Cells
3.62
article

How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?

Siyu Ye, Tang Chun-mei, Baoyin Yuan, Ning Wang, Xiaohan Zhang
article en
7 citations

Abstract

The development of efficient and stable hydrogen production technologies is crucial for global clean energy transition. Solid oxide electrolysis cells (SOECs) have emerged as a promising technology for green hydrogen production due to their high efficiency, low-cost catalysts, and excellent adaptability to renewable energy sources. However, significant challenges remain in materials design, interface engineering, and system integration. This perspective reviews recent advances in artificial intelligence (AI)-guided SOEC development, focusing on machine learning approaches for design of key materials. Furthermore, we highlight how AI technologies can address the key challenges in both single-cell performances and system-level integration with renewable energy sources. Looking forward, we outline strategic directions for advancing AI-driven SOEC development toward commercial implementation, which may offer valuable insights and experiences within the field of energy conversion and storage.

Journal of Materials InformaticsVol. 5(2)
Guangzhou University (CN)
Openalex Percentile: Top 7%
Advancements in Solid Oxide Fuel Cells
3.62
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How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell? — Siyu Ye, Tang Chun-mei, et al. · Journal of Materials Informatics (2025) | TGRS Research Map | TGRS