Symbolic transform optimized convolutional neural network model for high-performance prediction and analysis of MXenes hydrogen evolution reaction catalysts

Abstract

Two-dimensional MXenes materials are expected to be cost-effective and efficient catalysts for hydrogen evolution reaction (HER) due to their tunable surface electronic structure. To optimize its catalytic activity, this study proposes a data-driven framework (CROSST) to generate descriptors through feature engineering and integrate them into a convolutional neural network to predict the HER performance of transition-metal (TM) anchored bis-transition-metal carbon-nitride (TM-M′2M′′CNO2) catalysts. The STCNN model achieves a coefficient of determination of more than 0.93, showing the ability to predict the Gibbs free energy of hydrogen adsorption (ΔGH) with high accuracy. We explored the microscopic mechanisms of HER activity through density functional theory and machine learning analysis. It was found that TM anchoring altered the charge distribution of the MXenes material, elevated the atomic orbital occupancy of neighboring O sites, and weakened the O adsorption of H, thus affecting the HER activity. These results provide a theoretical basis for the design of high-performance MXenes HER catalysts and contribute new research perspectives.