A machine learning based method for hydrogen oxidation mechanisms classification in solid oxide cells

• A machine learning model was developed to identify the reaction mechanism. • Data were produced in the one-dimensional button cell model. • Electrochemical impedance spectroscopy serves as the reaction mechanism criterion. • The accuracy of identifying reaction mechanisms exceeds 80%. The investigation of the reaction mechanism in solid oxide cells can establish a theoretical basis for improving the electrode structure, optimizing operational conditions, and enhancing cell performance. The conventional approach to mechanism research involves generating the cell's performance curve through experiments or constructing a basic reaction model, which is intricate and susceptible to human error. In this study, we introduce a novel mechanism identification method based on machine learning. This model can extract kinetic details from electrochemical impedance spectra with high precision and categorize the four hydrogen oxidation mechanisms accurately. The outcomes demonstrate that utilizing advanced artificial intelligence tools can simplify complex traditional electrochemical analyses into automated black-box models, offering a promising solution to liberate researchers from expensive and laborious experiments.