Machine-Learning-Guided Prediction of CO Adsorption Energetics for the Rational Design of Methanol Oxidation Catalysts

Abstract

Understanding adsorption energetics is critical for the rational design of methanol oxidation reaction (MOR) catalysts. In this work, a machine-learning framework is developed to predict the CO adsorption energetics of mono-and bimetallic catalysts relevant to MOR. The model integrates density functional theory (DFT)-derived data with physically meaningful electronic and geometric descriptors, including work function, d-band center, coordination number, and electronegativity, achieving accurate and robust predictions. Feature correlation and distribution analyses confirm that the selected descriptors are statistically well-conditioned, while model comparison identifies optimized XGBoost as the most reliable predictor. SHAP-based interpretability analysis reveals that adsorption energetics are governed by a cooperative interplay between surface electronic potential, orbital interactions, and local coordination environment, highlighting pronounced nonlinear structure-property relationships. All descriptors used in this study are readily accessible from simple theoretical calculations or literature databases, enabling rapid screening of candidate catalysts without extensive experimental characterization. This work provides a practical machine-learning-driven strategy for accelerating the early-stage discovery and rational design of high-performance methanol oxidation catalysts.