Computational modeling of electrocatalyst reconstruction.

Area of Science:

• Computational materials science
• Electrocatalysis
• Surface chemistry

Background:

• Electrocatalyst reconstruction significantly impacts activity, selectivity, and stability.
• Computational methods are essential for understanding dynamic structural changes in electrocatalysts.
• Existing methods range from DFT to machine learning, offering insights across various scales.

Purpose of the Study:

• To provide a concise overview of computational methods for studying electrocatalyst reconstruction.
• To highlight how these approaches advance mechanistic understanding.
• To guide the development of next-generation predictive models.

Main Methods:

• Density functional theory (DFT)-based phase diagram analysis
• Global structure search
• Ab initio molecular dynamics
• Kinetic Monte Carlo (KMC) simulations
• Machine learning (ML)-accelerated simulations
• Multiscale modeling frameworks

Main Results:

• Computational strategies offer complementary insights into accessible states, restructuring pathways, and structural evolution.
• Recent progress in ML potentials and multiscale modeling extends simulations to greater chemical complexity.
• Key challenges include developing open-system models for interfacial exchange and dissolution-redeposition.

Conclusions:

• Computational tools are vital for understanding electrocatalyst reconstruction.
• Advancements in ML and multiscale modeling are enabling more realistic simulations.
• Future work should focus on open-system models to capture complex interfacial dynamics.