Machine-learning assisted screening of MXene-supported single-atom catalysts for oxygen reduction

Abstract

The strategic design of cost-effective and high-performance multifunctional electrocatalysts for the oxygen reduction reaction (ORR) is of great importance, as the ORR is a crucial half-reaction in proton-exchange membrane fuel cells (PEMFCs) and metal–air batteries. In this study, Ti₂NO₂ MXene and its derivatives with defective surfaces (O or Ti vacancies) are selected as supports for single-atom catalysts (SACs), and their ORR electrocatalytic performances under acidic conditions are systematically investigated using density functional theory (DFT) calculations. The calculations reveal that Pd/Ti₂NO₂ and Rh/Ti₂NO₂ exhibit outstanding ORR electrocatalytic activity, with an overpotential (η_ORR) of 0.31 V and 0.69 V, respectively, along with high selectivity against hydrogen evolution reaction (HER) competition. A significant correlation between ΔG_O2*/ΔG_OH* and η_ORR for TM/Ti₂NO₂ and TM/Ti₂NO₂-Ov SACs indicates that O₂* and OH* species serve as key intermediates throughout the ORR process. Furthermore, machine learning analysis reveals that the combination of the main descriptors, that is, the feature D generated by SISSO, the Bader charge of the single atoms, and the number of outermost d electrons of the single atoms, plays a critical role in ORR activity. This work is expected to provide valuable insights for the accelerated discovery of advanced ORR electrocatalysts and broaden the application potential of MXene-based materials in energy conversion and storage.