Bifunctional Oxygen Reduction/Evolution Reaction Activity of Transition Metal-Doped T-C3N2 Monolayer: A Density Functional Theory Study Assisted by Machine Learning

Designing efficient and cost-effective bifunctional electrocatalysts for the bifunctional oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) is crucial for sustainable and renewable energy technologies. In this study, we systematically investigate the potential of single transition metal (TM)-doped T-C3N2 as bifunctional ORR/OER electrocatalysts using density functional theory and machine learning. The results reveal that TM atoms can be stably incorporated into the N vacancy (TMN) and the central hexagonal hole (TMi) of T-C3N2, creating various coordination environments for the TM atoms, which can influence the ORR/OER electrocatalytic performance. The TM atom embedded in the central hexagonal hole (Cui) is a robust bifunctional ORR/OER electrocatalyst due to its low overpotentials (0.53 V for ORR and 0.52 V for the OER) and superior thermodynamic stability. The ORR/OER catalytic performance of Cui maintains well under the biaxial strain (−1% to +6%), as the ORR and OER overpotentials of Cui change slightly with the biaxial strain. Nevertheless, the ORR and OER overpotentials increase sharply once the biaxial compressive strain exceeds −1%. Hence, substrates with lattice constants equal to or larger than T-C3N2 are required to obtain good bifunctional ORR/OER activity in experimental equipment. Lastly, we employ the machine learning method with a gradient-boosted regression model to determine the origin of ORR and OER activity. The results indicate that the charge transfer of TM atoms (Qe) is the dominant descriptor for ORR activity, while the d-electron counts (Ne) and the d-band center (εd) are critical descriptors for OER. Our research highlights the efficiency of TM atom-doped T-C3N2 as bifunctional electrocatalysts and offers valuable insights for developing electrocatalysts for future clean energy conversion and storage applications.