Prediction of holey graphyne-supported single atom catalyst for nitrogen reduction reaction by interpretable machine learning and first-principles calculations

Abstract

The electrocatalytic nitrogen reduction reaction (NRR) with efficient and selective electrocatalysts has emerged as a promising alternative for sustainable ammonia synthesis. In this study, we designed a novel class of single-atom catalysts (SACs), denoted as TM@HGY, by embedding 3d, 4d, and 5d transition metals into synthesized holey graphyne (HGY), and investigated their potential for NRR using a combined approach of density functional theory (DFT) calculations and machine learning. Through stability assessments and a three-step screening strategy, Sc@HGY, V@HGY, and Mo@HGY were identified as promising NRR electrocatalysts. Notably, V@HGY exhibited an exceptionally low limiting potential of −0.16 V, which is superior to all the known NRR SACs supported by graphyne-family members. Machine learning (ML) analysis revealed that the Mendeleev number (Nm), group (G), and d-orbital radius (Rd) of the absorbed metal atom are the primary contributors to the structural stability and catalytic activity of these SACs, and clear strategies for optimizing catalyst design were further suggested based on their intrinsic relationships. This work reveals the significant potential of TM@HGY in NRR, providing powerful guidance for designing high-performance SACs in the field of sustainable ammonia synthesis.