Predicting the HER Activity of SACs on MXenes with Simple Features and Interpretable Machine Learning Models

Abstract

The efficient design of single-atom catalysts (SACs) for the hydrogen evolution reaction (HER) requires rapid and accurate screening methods able to handle 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 have created our own database based on DFT calculations using MXenes-based SACs for HER and we develop an interpretable machine learning (ML) model that predicts the hydrogen adsorption free energy (ΔG H ) 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 ΔG H . This ultimately leads to the development of a glass-box model, where we derive a transparent analytical equation that allows for direct prediction 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 the HER.