Two-Stage Feature Selection for Machine Learning-aided DFT-based Surface Reactivity Study on Single-Atom Alloys

This paper presents a feature-centric strategy for predicting adsorption energies of key CO 2 reduction reaction (CO 2 RR) adsorbates, CO and H species, utilizing density functional theory-based calculations for eight adsorption sites and considering alloying effects of nine transition metals at single-atom concentrations. Here, we explore a class of materials consisting of a majority host metal where individual atoms of a different element are dispersed called single-atom alloys (SAA). A total of eight feature selection methods are assessed within Gradient Boosting Regression and Linear Regression models. This study proposes a practical and effective two-stage approach that narrows down the initial 86 features to subsets of 10 and 7 for CO and H adsorption energy predictions, respectively, with the arithmetic mean of valence electrons (VE-am) feature consistently emerging as highly influential, validated through permutation and Shapley additive explanations-based feature importance analyses. The models exhibit robust performance on unseen data, indicating their generalization capability. The findings emphasize VE-am as a potential key machine learning feature for CO 2 RR on SAA surfaces and underline the effectiveness of the feature-centric approach in understanding feature impacts in machine learning models for CO 2 RR on SAA systems. Additionally, while other features based on structural, electronic and elemental properties may not individually impact the model significantly, their collective contribution plays a vital role in achieving more accurate adsorption energy predictions.