Abstract
Kinetic barriers under realistic solvation and potential conditions, known as critical in electrochemistry in recent years, have not been widely applied in the screening of electrocatalysts, mainly due to the high computational cost. Here, we demonstrate the establishment of quantitative relations between thermodynamics and kinetic barriers, which guides electrocatalyst screening from 51 candidates, taking single-atom@coinage-metal (M(1)@CM) alloys catalyzing electrochemical nitrogen reduction reaction (eNRR) as an example. For CM = Cu, Ag, and Au, separated linear relations are found between the free energy changes (ΔG) based on the computational hydrogen-electrode model and the kinetic barriers (ΔG (#) ) calculated from enhanced sampling of constant-potential ab initio molecular dynamics (cp-AIMD). The variations among Cu, Ag, and Au can be primarily attributed to differences in interfacial water orientation and surface charge under the calculated potential, properties governed by their respective work functions. Furthermore, a unified mapping from ΔG to ΔG (#) is found with a prediction error of about 0.05 eV across the three hosts using machine learning regression methods. Based on these relations, the high-active zone is identified, while the full path is calculated for the representative case Re(1)@Ag. Indeed, all barriers are no higher than 0.85 eV, significantly lower than other reported systems if barriers of all steps are examined. This work not only presents a screening strategy to quickly identify an eNRR catalyst with all-low kinetic barriers along the full path but also demonstrates how to establish and apply the quantitative relation between thermodynamics and cp-AIMD barriers, to significantly accelerate accurate screening of electrocatalysts.