( Invited ) Machine Learned Force Field Accelerated Modeling of Synthetic Pathways for Fe–N–C Fuel Cell Catalysts

Abstract

Iron–nitrogen–carbon (Fe–N–C) catalysts are among the most promising platinum-group-metal free electrocatalysts for the oxygen reduction reaction in proton-exchange membrane fuel cells. Despite significant experimental progress, rational improvement of Fe–N–C catalytic performance remains limited by incomplete understanding of the synthesis pathways governing active-site formation. In particular, the kinetics associated with high-temperature pyrolysis and Fe impregnation steps are difficult to access experimentally and with traditional atomistic simulation approaches at scale. Here, we leverage machine-learning foundation models to efficiently resolve transition states along candidate synthetic pathways for active site formation within graphitic domains. The foundation models are fine-tuned to Fe–N–C chemical environments using density functional theory reference data, enabling accurate and computationally tractable exploration of complex reaction landscapes. This approach allows systematic comparison of competing pathways and direct evaluation of kinetic barriers associated with active-site formation. By combining transition-state resolution with the scalability of foundation models, this work establishes a rapid, predictive framework for assessing how precursor chemistry and local coordination influence Fe–N–C site formation. The results demonstrate the potential of foundation-model-driven workflows to accelerate the design of carbon-based electrocatalysts and provide mechanistic guidance for synthetic optimization.