Machine Learning-Assisted Screening of Non-Adjacent Dual-Atom Catalysts on N-Doped Graphene: Insights Into the Electron "Storage-Feedback" Mechanism for N2 Reduction.

Conventional dual-atom catalysts (DACs) for the nitrogen reduction reaction (NRR) primarily rely on adjacent metal sites to achieve synergistic effects. However, this configuration often suffers from inherent limitations such as steric hindrance, restricted active site density, and difficulty in finely tuning electronic interactions between metal centers, which collectively hinder catalytic performance under practical conditions. To address these challenges, we propose a novel class of bimetallic electrocatalysts featuring non-adjacent dual-atom sites anchored on N-doped graphene (denoted as TMATMB@NGII), in contrast to the widely studied adjacent-site counterparts (TMATMB@NGI). Through a combined approach of density functional theory (DFT) calculations and machine learning (ML), we systematically predict 784 transition metal combinations and identify V-Os@NGII as a highly promising catalyst, while Mn-Cd@NGII is selected as a representative ML-predicted candidate for complete-pathway validation. Mechanistic investigations reveal an unprecedented electron "storage-feedback" mechanism, which differs fundamentally from the conventional synergistic effects observed in adjacent-site systems and provides a new paradigm for understanding long-range electronic interactions in dual-atom catalysis. Our work not only introduces a non-adjacent dual-site architecture but also establishes an integrated DFT-ML framework for the rational design of advanced electrocatalysts beyond traditional coordination geometries.