Artificial intelligence and quantum chemistry unveil next-generation "dual-modulated" catalysts for fuel cells
Peer-Reviewed Publication
Updates every hour. Last Updated: 27-Oct-2026 16:16 ET (27-Oct-2026 20:16 GMT/UTC)
The Fe–N–C single-atom catalyst represents a promising candidate for promoting the oxygen reduction reaction (ORR), which is crucial for fuel cell applications; yet, identifying optimal modification strategies to enhance its activity and stability remains challenging. Herein, the modulation of Fe–N–C catalysts via in-plane heteroatom doping and axial coordination is systematically investigated using integrated density functional theory (DFT) and machine learning (ML) approaches. The analysis reveals that axial ligands have a more profound influence on ORR performance than in-plane dopants, primarily by modulating the Fe dz2 orbital and weakening *OH adsorption. Through interpretable descriptors extracted from ML models, the key electronic and geometric properties governing catalyst behavior are identified, and several novel dual-modified candidates with enhanced activity relative to pristine Fe–N–C are subsequently predicted and validated by DFT calculations. This work provides a unified mechanistic and data-driven framework for accelerating the design of high-performance Fe–N–C ORR electrocatalysts.
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