Modeling Operando Electrochemical CO₂ Reduction.

Area of Science:

• Computational electrochemistry
• Materials science
• Catalysis

Background:

• Density functional theory (DFT) and the computational hydrogen electrode (CHE) have driven electrocatalytic reaction modeling for two decades.
• Linear scaling relationships, volcano plots, and overpotential diagrams are established theoretical tools.
• Operational conditions introduce complexities like morphological changes and transient intermediates, challenging current models.

Purpose of the Study:

• To highlight novel computational methodologies for modeling electrochemical CO2 reduction (eCO2R) and hydrogen evolution (HER).
• To integrate atomic-scale insights with bulk electrolyte phenomena.
• To address limitations of static models by incorporating dynamic operational effects.

Main Methods:

• Combined *ab initio* calculations and machine learning to model surface reconstruction and identify active sites.
• Coupled microkinetic modeling with *ab initio* and machine learning for mechanism elucidation.
• Utilized DFT and machine learning to interpret *Operando* spectroelectrochemical data (e.g., Raman, EXAFS).

Main Results:

• Demonstrated partial reproduction of surface reconstruction under operating conditions.
• Showcased the synergy between computational techniques and *Operando* characterization for mechanistic understanding.
• Reviewed the significant impact of electrolyte composition and mass transport on reaction performance.

Conclusions:

• Advanced computational approaches are crucial for understanding dynamic electrocatalytic systems.
• Integrating multiple scales (atomic to bulk electrolyte) and techniques (*ab initio*, ML, *Operando* spectroscopy) provides deeper mechanistic insights.
• Future computational modeling must address operational complexities to accelerate catalyst discovery for eCO2R and HER.