Artificial intelligence accelerates the development of fuel cell materials

The review article discusses the applications of artificial intelligence (AI) in the field of fuel cells, focusing on its principles, applications, existing challenges, and future directions in accelerating materials development. The article is published in AI & Materials and has the potential to advance the development of AI in the field of fuel cells, improving the efficiency of fuel cell material design.

Fuel cells play a pivotal role in the utilization of hydrogen energy, yet the technology currently encounters numerous challenges, particularly concerning the enhancement of various component materials. Consequently, modifying existing materials and designing novel ones have emerged as critical research areas in recent years. However, traditional trial-and-error approaches are increasingly inadequate for rapid development due to their inefficiency and high costs. Fortunately, the rapid advancements in artificial intelligence (AI) have introduced innovative solutions, enabling efficient enhancement of material properties and optimization of system control, thereby significantly accelerating the development process of fuel cells.

This article provides a comprehensive overview of the working principles and mechanisms of different types of fuel cells, while also conducting an in-depth analysis of the specific material requirements for each component. It then introduces the fundamental concepts of AI and its applications in materials science, elucidating the solutions AI offers to common problems in material design, including data collection, performance prediction, classification, and factor analysis. The article further details the specific processes involved in applying machine learning techniques to material design and summarizes the main methods for obtaining material data.

The article subsequently explores machine learning applications in fuel cell material design through several case studies. For instance, in proton exchange membrane fuel cells (PEMFC), Zhen et al. developed a physical niche genetic (PNG) machine learning program to predict the stability of platinum-nickel (Pt-Ni) alloy nanoparticle frameworks with an error margin below 0.13 eV. This model was used to predict the stability of 2.5 × 10⁵ candidate structures, identifying Pt₄₃Ni₄₂ as the most stable configuration.

In the solid oxide fuel cell (SOFC) field, Zhai et al. utilized data from 89 materials to create a model predicting area-specific resistance (ASR) using nine descriptors, including ionic Lewis acid strength. This model was then employed to predict the performance of unstudied materials, leading to the successful synthesis of four high-performance perovskite oxides. Additionally, density functional theory (DFT) calculations revealed that the polarization distribution of ionic Lewis acids reduced oxygen vacancy formation energy and migration barriers, explaining the role of Lewis acids as descriptors and the mechanism behind enhanced redox activity. This work presents a comprehensive approach that integrates machine learning, experimental synthesis, and theoretical analysis.

The final section of the article examines the five major challenges faced by machine learning in its current applications: lack of datasets, difficulty in feature construction, insufficient model generalization ability, and interpretability issues. The article also outlines corresponding solutions, such as augmenting datasets through transformations like rotation, scaling, and cropping, employing feature selection algorithms like recursive feature elimination to identify significant features, enhancing model generalization via hyperparameter tuning and regularization, and applying methods like LIME and SHAP for model interpretability.

In conclusion, this article provides a systematic review of the application of artificial intelligence in the design of fuel cell materials, such as electrolytes, electrodes, and catalysts. It outlines general strategies and structured workflows for data-driven material discovery. Furthermore, it discusses the challenges AI faces, such as data scarcity, and proposes potential solutions like SHAP and ensemble learning, which will facilitate future research in leveraging AI to assist fuel cell material development.

This review ”Applications of artificial intelligence in materials research for fuel cells” was published in AI&Materials.

Liu H, Guo H, Gao Z, Pan H, Zheng J, et al. Applications of artificial intelligence in materials research for fuel cells. AI Mater. 2025(1):0003, https://doi.org/10.55092/aimat20250003.