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

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J. Mater. Inf. 2025;5:[Accepted].
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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.

Keywords

Hydrogen energy, solid oxide electrolysis cell, anode material, machine learning

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Yuan B, Zhang X, Tang C, Wang N, Ye S. How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell? J. Mater. Inf. 2025;0:[Accept]. http://dx.doi.org/10.20517/jmi.2024.106

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© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Journal of Materials Informatics
ISSN 2770-372X (Online)
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