Predicting the HER activity of SACs on MXenes with simple features and interpretable machine learning models

The efficient design of single-atom catalysts (SACs) for hydrogen evolution reaction (HER) requires rapid and accurate screening methods capable of handling a large number of candidate materials. Traditional density functional theory (DFT) calculations, while reliable, are computationally expensive, necessitating alternative approaches for high-throughput screening. In this work, we create our own database based on DFT calculations using MXene-based SACs for HER and develop an interpretable machine learning (ML) model that predicts hydrogen adsorption free energy (ΔGH) with an accuracy of 0.17 eV, relying solely on simple, non-DFT-calculated features. Our approach systematically transitions from a black-box model to a grey-box model, incorporating feature importance analysis to identify key descriptors influencing ΔGH. This ultimately leads to the development of a glass-box model, where we derive a transparent analytical equation that allows for direct prediction of ΔGH using raw feature values. Importantly, the final model does not require data standardization or complex computational simulations, making it highly accessible for experimental researchers and enabling rapid, first-order screening of MXene-based SACs for HER.