Heterojunction of MXenes and MN-graphene: Machine Learning to Accelerate the Design of Bifunctional Oxygen Electrocatalysts

Oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are essential for the development of excellent bifunctional electrocatalysts, which are key functions in clean energy production. The emphasis of this study lies in the rapid design and investigation of 153 MN-graphene (Gra)/ MXene (MNO) electrocatalysts for ORR/OER catalytic activity using machine learning (ML) and density functional theory (DFT). The DFT results indicated that CoN-Gra/TiNO had both good ORR (0.37 V) and OER (0.30 V) overpotentials, while TiN-Gra/MNO and MN-Gra/CrNO had high overpotentials. Our research further indicated orbital spin polarization and d-band centers far from the Fermi energy level, affecting the adsorption energy of oxygen-containing intermediates and thus reducing the catalytic activity. The ML results showed that the gradient boosting regression (GBR) model successfully predicted the overpotentials of the monofunctional catalysts RhN-Gra/TiNO (ORR, 0.39 V) and RuN-Gra/WNO (OER, 0.45 V) as well as the overpotentials of the bifunctional catalyst RuN-Gra/WNO (ORR, 0.39 V; OER, 0.45 V). The symbolic regression (SR) algorithm was used to construct the overpotential descriptors without environmental variable features to accelerate the catalyst screening and shorten the trial-and-error costs from the source, providing a reliable theoretical basis for the experimental synthesis of MXene heterostructures.