Quantifying robustness of DFT predicted pathways and activity determining elementary steps for electrochemical...

Area of Science:

• Computational chemistry
• Materials science
• Electrochemistry

Background:

• Density functional theory (DFT) is crucial for catalyst screening.
• Catalyst activity is often predicted using scaling relations and Sabatier-type volcano plots.
• Descriptor choice significantly impacts the accuracy of predicting catalytic activity and reaction selectivity.

Purpose of the Study:

• To develop a framework for quantifying confidence in identifying potential-determining steps and reaction pathway selectivity.
• To introduce 'classification efficiency' as a metric for ranking descriptors.
• To apply this framework to oxygen reduction and evolution reactions.

Main Methods:

• Developed a quantitative metric, 'classification efficiency,' to assess descriptor performance.
• Applied DFT calculations to analyze oxygen reduction and oxygen evolution reactions.
• Evaluated various descriptor combinations for predicting selectivity and limiting steps.

Main Results:

• Identified ΔG_OOH as the optimal descriptor for classifying between 2e^- and 4e^- oxygen reduction.
• Found ΔG_OH and ΔG_OOH to have comparable performance in identifying the limiting step for 4e^- oxygen reduction.
• Determined that {ΔG_OOH*,ΔG_O } and {ΔG_OH ,ΔG_O } are highly efficient descriptors for classifying 2e^- and 4e^- water oxidation.

Conclusions:

• The proposed 'classification efficiency' framework provides a robust method for descriptor selection in catalyst screening.
• The methodology offers improved mechanistic insights for multi-electron electrochemical reactions.
• This approach can be extended to other reactions, including CO₂ and N₂ reduction.