Enhancing <scp>HER</scp> catalyst screening of modified <scp>MXenes</scp> through <scp>DFT</scp> and machine learning integration

Abstract MXenes doped with non‐metallic and transition metal elements exhibit remarkable potential as catalysts in the hydrogen energy. Nonetheless, efficiently identifying viable materials from a vast array of candidates remains a formidable challenge. Here, we conducted density functional theory (DFT) calculations to obtain the hydrogen adsorption free energy () of 78 types of doped TiVCO 2 MXene catalysts. Then we employed machine learning models to categorize the values of the 78 catalysts, resulted in an accurate model which only uses 7 readily available elemental features but has an impressive accuracy of 93.6%. Our model successfully predicting 5 TiVCO 2 catalysts doped with S with superior performance, subsequently validated through DFT calculations. This classification methodology not only evaluates the range of effectively but also facilitates qualitative prediction and screening of catalysts, presenting a novel approach for catalytic systems with limited available data.