Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.

Area of Science:

• Computational chemistry
• Electrocatalysis
• Materials science

Background:

• Electrocatalytic reactions at solid-liquid interfaces are crucial for energy conversion.
• Accurate modeling requires integrating quantum mechanics with the electrochemical environment.

Purpose of the Study:

• To review theoretical frameworks and computational techniques for modeling electrocatalytic reactions.
• To clarify assumptions, approximations, and practical considerations for researchers.

Main Methods:

• Focus on first-principles approaches, particularly density functional theory (DFT).
• Discusses thermochemical models (e.g., computational hydrogen electrode) and potential-dependent DFT.
• Highlights machine learning (ML) for catalyst screening and ML-based force fields.

Main Results:

• Examines treatment of thermodynamics, electrode bias, solvation, electrolyte screening, and kinetics.
• Compares different methods regarding reliability and computational cost.
• ML approaches offer efficient simulations with near-first-principles accuracy.

Conclusions:

• Selecting appropriate modeling methods is crucial for physically meaningful and computationally tractable simulations.
• ML advancements promise efficient, accurate modeling of complex electrochemical systems.
• Understanding underlying assumptions is key to reliable electrocatalysis modeling.