Design and screening of single transition-metal atom anchored on G/BP heterostructure as NORR electrocatalysts: A DFT and machine learning study.

Electrocatalytic NO reduction reaction (NORR) enables the conversion of hazardous NO gases into valuable NH₃ under ambient conditions. However, due to the slow reaction kinetics and low Faraday efficiency of NORR, it is essential to explore active and selective catalysts to promote NORR enhancement. Herein, a series of transition-metal atoms anchored on the graphene and blue phosphorus (TM-G/BP) heterostructures as efficient NORR catalysts are systematically examined using Density Functional Theory (DFT) calculations. Consequently, the Fe-G/BP and Rh-G/BP heterostructures were screened as effective NORR electrocatalysts for their high activity and selectivity with UL of 0.31 and 0.46 V, respectively. We also investigated the origin of NORR activity. The electronic analysis reveals that Fe-3d and Rh-4d orbitals can be well hybridized with the NO-2p orbitals, sufficiently activating the adsorbed NO molecule. In addition, SISSO as a machine learning approach is utilized to construct descriptors of UL that considers properties of TM atoms, coordination environment, changes in key step free energy to predict activity. The finally constructed descriptor exhibited a high correlation coefficient of 0.90 and a mean squared error as low as 0.06. Our work not only identifies promising NORR catalysts in TM-G/BP heterostructures but also provides valuable insights for the mechanism of NO-to-NH3 conversion.