How AI guided the development of green hydrogen production: in the case of solid oxide electrolysis cell?
Year: 2025
Type: article
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.
Source: Journal of Materials Informatics
Authors: Baoyin Yuan, Xiaohan Zhang, Chunmei Tang, Ning Wang, Siyu Ye
Institution: Guangzhou University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408723843