Machine Learning-Assisted DFT Screening of Nitrogen-Doped Graphene Diatomic Catalysts for Nitrogen Reduction Reaction.

ABSTRACT: This research seeks to investigate extremely efficient catalysts for the nitrogen reduction process (NRR), utilizing machine learning (ML)-aided density functional theory (DFT) computations. Specifically, we investigate dual transition metal atoms anchored on hexagonal nitrogen-doped graphene (TM₁-TM₂@N₆G) as prospective high-activity catalysts for the NRR. The findings indicate that the synergistic effect of dual transition metal atoms in the TM₁-TM₂@N₆G catalyst overcomes the intrinsic constraints of the linear relationship among intermediates, facilitating the activation and adsorption of N₂, thereby exhibiting significant potential for ammonia synthesis through N₂ reduction. Particularly, four catal

SUBMITTER: Wang X 

PROVIDER: S-EPMC12565872 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature