Enhancing HER catalyst screening of modified MXenes through DFT 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.