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Graph Neural Network-Based Composition Recommendation for Solid Oxide Fuel Cells Using Full-Cycle Data
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DOI：10.1109/ACCESS.2026.3664879.png)
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Abstract
En 中文
The design of high-performance solid oxide fuel cell (SOFC) materials remains challenging due to the complex coupling between composition, processing conditions, and electrochemical performance. In this study, a data-driven composition design framework based on a graph neural network (GNN) is proposed using full-cycle experimental data. Here, full-cycle data refer to an integrated dataset linking raw material composition, processing conditions (mixing, coating, and heat treatment), and electrochemical performance. A dataset was constructed from LaFeO3-based SOFC anode materials measured under different cell configurations and operating temperatures (700– $900~^{\circ }$ C). Based on this dataset, a GNN-based composition recommendation model was developed, in which compositional variables were represented using a K-nearest neighbor graph structure. The model was trained to recommend suitable anode compositions for given operating conditions specified by the target electrochemical performance. For prospective validation, the proposed model was applied to seven operating conditions, and 21 recommended anode compositions were successfully fabricated and tested. The experimentally measured maximum power densities exhibited an average deviation of 9.35% from the target performance values. These results indicate that the proposed GNN-based framework provides a practical data-driven tool for supporting SOFC composition design under limited experimental data.
Keywords:
Solid oxide fuel cell (SOFC)
graph neural network (GNN)
composition recommendation
full-cycle data
generative model augmentation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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