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

Abstract

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 (TM_N) and the central hexagonal hole (TM_i) 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 (Cu_i) 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 Cu_i maintains well under the biaxial strain (−1% to +6%), as the ORR and OER overpotentials of Cu_i 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 (Q_e) is the dominant descriptor for ORR activity, while the d-electron counts (N_e) and the d-band center (ε_d) 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.