LAUSR

The growing utilization of carbon materials in infrared detection necessitates the development of precise models concerning interfacial properties within carbon frameworks. However, the inherent structural complexity of these carbon frameworks complicates the understanding of carrier transport mechanisms at interfaces. This study systematically probes carrier transport at micro/nano interfaces in 0D/3D composite carbon frameworks, uncovering that the spatial arrangement of heteroatoms in nitrogen‐doped graphene quantum dots (N‐GQDs) affects carrier transport properties. Specifically, employing C3N quantum dots (C3N QDs) that exhibit D6h symmetry of N atoms embedded within a sp2 carbon matrix results in a substantial reduction in the carrier transfer energy barrier between QDs and 3D‐graphene. This enhancement markedly boosts interfacial charge exchange efficiency. This finding offers a strategy for optimizing the performance of carbon‐based optoelectronic devices. It delineates a clear link between atomic‐level structural attributes and macroscopic performance, laying a theoretical foundation for future machine learning‐assisted design of carbon materials. Furthermore, by integrating electron cloud density analysis with lattice matching principles, this research establishes a novel framework for elucidating the structure‐performance relationship in carbon frameworks. This framework will facilitate the interpretation of machine learning predictions, thereby opening up new pathways for machine learning‐driven modeling of complex carbon‐based material systems.