Selecting effective catalysts for the hydrogen evolution reaction (HER) among MXenes remains a complex challenge. While machine learning (ML) paired with density functional theory (DFT) can streamline this search, issues with training data quality, model accuracy, and descriptor selection limit its effectiveness. These hurdles often arise from incomplete understanding of the catalytic mechanisms. Here, we introduce a mechanism-guided descriptor (δ) for HER, designed to enhance catalyst screening among ordered transition metal carbonitride MXenes. This descriptor integrates structural and energetic characteristics, derived from an in-depth analysis of orbital interactions and the relationship between Gibbs free energy of hydrogen adsorption (ΔGH) and structural features. The proposed model (ΔGH = -0.49δ – 2.18) not only clarifies structure-activity links but also supports efficient, resource-effective identification of promising catalysts. Our approach offers a new framework for developing descriptors and advancing catalyst screening.
