Machine-learning assisted screening 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 ORR is a crucial half-reaction in proton-exchange membrane fuel cells (PEMFCs) and metal-air batteries. In this study, Ti2NO2 MXene and its derivates with defective surfaces (O or Ti vacancy) 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 the Pd/Ti2NO2 and Rh/Ti2NO2exhibit outstanding ORR electrocatalytic activity, with overpotential (ƞORR) of 0.31 V and 0.69 V, respectively, along with high selectivity against hydrogen evolution reaction (HER) competition. A significant correlation between ΔGO2*/ΔGOH* and ƞORR for TM/Ti2NO2andTM/Ti2NO2-Ov SACs indicate that O2* and OH* species serve as key intermediates throughout the ORR process. Furthermore, machine learning analysis reveal that the combination of the main descriptors, that is, the feature D generated by SISSO, the number of outermost d electrons of the single atoms, and the Bader charge of the single atoms, plays critical role for 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.