Fine-Tuning Dual Single-Atom Metal Sites on Graphene toward Enhanced Oxygen Reduction Reaction Activity
Evan Xie, Xijun Wang
Abstract
The oxygen reduction reaction (ORR) remains at the forefront of research in diverse energy and sustainability domains. While graphene-supported single-atom catalysts (SACs) have garnered attention for optimizing ORR efficiency, tailoring the interactions between adjacent single-atom sites presents intricate challenges. In this study, we leveraged density functional theory (DFT) calculations and cutting-edge machine learning (ML) techniques to explore 144 graphene-supported SACs, featuring interacting M 1 –N 4 and M 2 –N 4 moieties (M 1, M 2 = Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag Ir, Pt, Au), denoted as M 1 –M 2 . By tailoring these interactions, we discovered 13 exceptional SACs outperforming the benchmark catalyst Fe(OH)–N 4, including the best-performing Fe–Pd and several non-noble-metal SACs like Fe–Ag, Ag–Cu, and Ag–Ag. Venturing further, our ML models effectively capture the correlation between single-atom metal properties and overpotential, offering tools for rational electrocatalyst design. Our study illuminates the path to efficient SAC-catalyzed ORR, fostering a sustainable, energy-efficient future.