A Brønsted–Evans–Polanyi Relationship-Guided Machine Learning Model for Predicting H ₂ Activation Mechanism over Indium Oxide Surfaces across Various Oxygen Vacancies Coverages

H 2 activation mechanism governs the activity, selectivity, and stability of CO 2 hydrogenation over In 2 O 3 surfaces, which varies with the coverages of oxygen vacancies under the realistic reaction conditions. Herein, we systematically map H adsorption and H 2 dissociation on cubic In 2 O 3 (111) across a broad range of oxygen vacancy coverages, explicitly distinguishing heterolytic cleavage at In–O pairs from homolytic cleavage at In–In pairs. Our results demonstrate that these two mechanisms follow distinct kinetic regimes rather than a universal Brønsted–Evans–Polanyi (BEP) relationship. The BEP slopes reveal that the descriptors of the initial state are more informative for heterolytic barriers, while those of final state better capture homolytic barriers. This mechanistic insight effectively narrows the candidate descriptor pool for predicting activation barriers across various surface states. SISSO and benchmark machine learning models consistently support this state dependent descriptor preference, highlighting the underlying chemical necessity of matching descriptors to TS character. Our results also reveal that H 2 activation on catalytically relevant pristine and moderately reduced surfaces is mainly achieved through heterolytic cleavage at In–O sites, while the homolytic cleavage at In–In sites is promoted and becomes prominent under deep reduction. However, excessive reduction does not monotonically promote H 2 dissociation. This work provides a physically interpretable protocol for mechanism-aware descriptor screening to elucidate hydrogen activation on metal oxides, guided by mechanistically resolved BEP relationships.