Dynamics and Kinetic Exploration of Oxygen Reduction Reaction at Fe-N4/C-water Interface Accelerated by Machine Learning Force Field

Abstract

Understanding the oxygen reduction reaction (ORR) mechanism and accurately characterizing the reaction interface are essential for improving fuel cell efficiency. We developed an active learning framework combining utilized machine learning force fields and enhanced sampling to explore dynamics and kinetics of ORR on Fe-N4/C under a fully explicit solvent model. Different possible reaction paths have been explored and the O2 adsorption process is confirmed as the rate-determining step of ORR at the Fe-N4/C-water interface, which needs to overcome a free energy barrier of 0.39 eV. By statistical analysis of solvent configurations for proton-coupled electron transfer (PCET) processes, it is revealed that the configurations of interface water remarkably influence the reaction efficiency. More hydrogen bonds and longer lifetime facilitates the PCET reactions and even make them barrierless. Our theoretical framework highlights the significance of solvent configurations in determining free energy barriers, and offers new insights into the reaction mechanism of ORR on Fe-N4/C catalysts.