Computational exploration of codoped Fe and Ru single-atom catalysts for the oxygen reduction reaction

Abstract

The oxygen reduction reaction (ORR) is essential in a range of energy conversion and storage technologies, including fuel cells and metal–air batteries. Single-atom catalysts (SACs), characterized by isolated metal atoms especially in doped graphitic substrates, have emerged as promising ORR catalysts due to their unique electronic and geometric properties. We employ virtual high-throughput screening (VHTS) with density functional theory and machine learning (ML) to explore the potential of codoped SACs with Fe and Ru centers for optimizing ORR reaction energetics. We also develop ML models, trained on VHTS data, that offer increased predictive accuracy of reaction energetics, surpassing the capabilities of conventional linear free energy relationship approaches. The results underscore codoping as an effective strategy for tuning SAC properties, enabling the rational design of high-performance ORR catalysts.