<![CDATA[This study explores the prediction of Hydrogen Evolution Reaction (HER) performance in Dynamic Magnetic Field (DMF) assisted water electrolysis using Artificial Neural Networks (ANN). The integration of ANN models with experimental data from DMF-assisted electrolysis provides valuable insights into the complex interplay between magnetic fields and electrochemical processes. The results show significant enhancements in HER rates compared to conventional electrolysis, with static magnetic fields also contributing to performance improvements. The ANN models developed exhibit high accuracy in predicting HER performance under varying DMF rotational speeds, as evidenced by low Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and high R-squared values, demonstrating their strong predictive power and reliability. However, caution is advised regarding overfitting, and future research should focus on incorporating techniques like regularization and cross-validation to enhance model generalization. This study lays the foundation for further optimization of efficient hydrogen production technologies in the context of sustainable energy solutions.]]>
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