Authors: Charlie Meisel | Jake Huang | Long Le | You-Dong Kim | Sophia Stockburger | Zhixin Luo | Tianjiu Zhu | Zehua Wang | Zongping Shao | Ryan O’Hayre | Neal P. Sullivan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Data-Driven Insights into Protonic-Ceramic Fuel Cell and Electrolysis Performance Cell reproducibility remains a significant challenge for emerging proton-conducting ceramic electrochemical fuel cell and electrolyzer technologies. This study investigates the factors contributing to cell-to-cell performance variation using a relatively large dataset of electrochemical data and machine learning models. Gaussian Process and Random Forest Regressor machine learning models were utilized to analyze 86 cells for fuel cell performance and 84 cells for electrolysis performance. The study focuses on BaCe 0.4 Zr 0.4 Y 0.1 Yb 0.1 O 3-δ (BCZYYb4411)+NiO | BCZYYb4411 | BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ (BCFZY) material sets for the negatrode, electrolyte, and positrode, respectively. Key processing and morphological parameters impacting performance were identified. Machine learning models can elucidate the complex interrelationships between processing parameters, cell characteristics, and electrochemical performance. Machine learning models develop pattern recognition from prior data and attempt to minimize error in future predictions based on unseed input data, allowing the models to identify complex relationships that humans would not be able to independently determine. Gaussian Process models are probabilistic, non-parametric machine learning tools that predict probability distributions over potential outcomes rather than single target values. Random Forest Regressor models predict target values using an ensemble of decision trees. Both these models excel at fitting non-linear relationships with relatively low prediction error. The electrolyte thickness to grain size ratio emerged as a critical factor for both fuel cell and electrolysis performance, with maximum gains at ratios &lt; 1. A NiO particle size threshold of ~6 μm was identified, below which performance increases markedly. Evaporating organics from the electrolyte spray or positrode application process before sintering may improve performance significantly, but the extent of this improvement remains uncertain. The optimal BCFZY positrode thickness for fuel cell performance is 20-25 μm. Higher ambient humidity levels in the laboratory are correlated with higher electrolysis performance. Fuel cell performance is primarily influenced by positrode microstructure and attributes, though the electrolyte morphology also has a significant impact. Optimizing the positrode microstructure can bring the largest benefit to fuel-cell performance through reduced polarization resistances. Electrolysis performance is strongly governed by electrolyte microstructure. Improving electrolyte microstructure and conductivity to reduce ohmic resistance greatly benefits electrolysis performance. When utilizing the same material sets across many proton-conducting ceramic electrochemical cells, ohmic and polarization resistance are uncorrelated with each other. Figure 1 Random forest ; Electrolysis ; Fuel cells ; Process (computing) ; Variance (accounting) ; Gaussian process ; Support vector machine ; Non-blocking I/O ; Electrolytic process Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials