Identifying the Activity Origin of a Cobalt Single-Atom Catalyst for Hydrogen Evolution Using Supervised Learning

Single-atom catalysts (SACs) have become the forefront of energy conversion studies, but unfortunately, the origin of their activity and the interpretation of the synchrotron spectrograms of these materials remain ambiguous. Here, systematic density functional theory computations reveal that the edge sites—zigzag and armchair—are responsible for the activity of the graphene-based Co (cobalt) SACs toward hydrogen evolution reaction (HER). Then, edge-rich (E)-Co single atoms (SAs) were rationally synthesized guided by theoretical results. Supervised learning techniques are applied to interpret the measured synchrotron spectrum of E-Co SAs. The obtained local environments of Co SAs, 65.49% of Co-4N-plane, 13.64% in Co-2N-armchair, and 20.86% in Co-2N-zigzag, are consistent with Athena fitting. Remarkably, E-Co SAs show even better HER electrocatalytic performance than commercial Pt/C at high current density. Using the joint effort of theoretical modeling, thorough characterization of the catalysts aided by supervised learning, and catalytic performance evaluations, this study not only uncovers the activity origin of Co SACs for HER but also lays the cornerstone for the rational design and structural analysis of nanocatalysts.