Screening of Electrocatalysts at the Level of Kinetic Barriers under Realistic Potential and Solvation: eNRR as a Case Study

Kinetic barriers under realistic solvation and potential conditions have been found to play critical roles in electrochemical reactions, however they have not been applied in massive screening of electrocatalysts mainly due to the high computational cost to obtain them. Here we demonstrate how to establish a quantitative relation between thermodynamics and kinetic barriers, and use it to guide electrocatalyst screening from a large number of candidates, taking single-atom@coinage-metal (M1@CM) alloys catalyzing electrochemical nitrogen reduction reaction (eNRR) as an example. For CM = Cu or Ag or Au, separated linear relations are found, indicating good scaling relations between the free energy changes based on the computational hydrogen electrode model and the kinetic barriers calculated from the constant-potential hybrid-solvation dynamic model. Furthermore, we show a unified mapping from ΔG to Ea is accessible with prediction error about 0.05 eV across the three hosts using machine learning regression methods. Based on these relations, the high-active zone is identified: Re1@Ag, Mo1@Ag, and Re1@Cu are predicted to have the highest eNRR activity, where the representative Re1@Ag is selected to assess kinetic barriers of the full eNRR path. Indeed it exhibits all barriers 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 new screening workflow to find an extraordinary eNRR catalyst, but also demonstrates how to employ the quantitative relation between thermodynamics and kinetic barriers from constant-potential ab initio molecular dynamics to significantly accelerate accurate screening of electrocatalysts.