Аңдатпа
Accurate prediction of hydrogen evolution reaction (HER) performance under varying operating conditions conventionally requires extensive electrochemical measurements or computationally intensive simulations. To address this challenge, an experimentally validated framework integrating computational fluid dynamics (CFD) and machine learning (ML) was developed for mechanistic analysis and HER performance prediction of Ba 0.5 Ca 0.5 CoO 3 perovskite electrocatalyst. A 3D multiphysics CFD model was established by coupling fluid flow, electrochemical kinetics, and ionic transport. The simulated polarization curves showed good agreement with experimental measurements across a wide range of temperatures (25-65°C) and electrolyte concentrations (1-5 M KOH). The validated CFD model was subsequently used to generate a comprehensive dataset for training three ML models, namely Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Ensemble Learning. All developed models demonstrated excellent predictive capability, achieving R 2 > 0.985, with GPR slightly outperforming the other models by showing a narrower error distribution. The GPR model was further employed to predict HER current density at previously unseen operating conditions. It exhibited strong agreement with CFD predictions under the sampled operating conditions (R 2 > 0.998), demonstrating robust interpolation capability and its potential as a computationally efficient surrogate for CFD simulations. Furthermore, permutation feature importance (PFI) analysis identified applied potential as the most influential operating parameter for predicting HER current density. The proposed hybrid CFD-ML framework enables rapid and computationally efficient prediction of the experimentally anchored CFD response within the investigated operating domain.