Hydrogen adsorption energy trends in Mo/WXY (X, Y = S, Se, Te) regular and Janus TMD monolayers: a first-principles and machine learning study

Abstract

The hydrogen adsorption behavior on two-dimensional (2D) transition metal dichalcogenides (TMDs) plays a key role in their photocatalytic properties. Using density functional theory (DFT), we investigate H adsorption energy (E_ads) trends in regular MX₂ and Janus MXY TMDs (M = Mo/W, X, Y = S/Se/Te). Our analysis identifies energetically favorable adsorption sites, revealing non-uniform site preferences influenced by covalency differences and atomic radii. Notably, Janus MXY TMDs exhibit significantly lower E_ads due to their built-in dipole moment, with stronger adsorption on surface atoms of higher electronegativity (EN). We develop machine learning (ML) models in order to predict E_ads based on the DFT dataset. Four energetic descriptors are used. Linear regression (LR) performs poorly with an R² score of 0.31, but partitioning the data by adsorption surface improves its accuracy (R² > 0.83), indicating a piecewise linear trend. Non-linear models, support vector regression (SVR) and multilayer perceptron regression (MLPR), capture this behavior more effectively, with one-hidden-layer MLPR achieving the highest accuracy (R² = 0.98). These results underscore the surface-dependent nature of H adsorption and highlight the effectiveness of our ML approach in predicting E_ads, offering a reliable alternative to complex theoretical simulations for the hydrogen evolution reaction.