A DFT-based genetic algorithm search for AuCu nanoalloy electrocatalysts for CO₂ reduction

Area of Science:

• Computational materials science
• Nanotechnology
• Catalysis

Background:

• Gold-copper (AuCu) nanoalloys are potential electrocatalysts for CO2 reduction.
• Determining the most stable structures and stoichiometry of nanoalloys is crucial for optimizing their performance.
• Accurate calculation of adsorption energies is essential for understanding catalytic mechanisms.

Purpose of the Study:

• To determine the most stable structure and stoichiometry of a 309-atom icosahedral AuCu nanoalloy.
• To develop an adsorbate-dependent correction scheme for accurate adsorption energy calculations.
• To evaluate the potential of AuCu nanoalloys as electrocatalysts for CO2 reduction.

Main Methods:

• Density Functional Theory (DFT) combined with a genetic algorithm (GA) approach.
• Development and application of an adsorbate-dependent correction scheme using a localized LCAO-basis set.
• Calculation of adsorption energies on pure Cu, pure Au, and mixed AuCu nanoalloy surfaces.

Main Results:

• The most stable 309-atom icosahedral AuCu nanoalloy features a copper core and a gold surface (Cu135@Au174).
• A computationally efficient LCAO-based method was developed for accurate adsorption energy calculations.
• The mixed Cu135@Au174 nanoalloy exhibits adsorption energies comparable to pure gold, with enhanced structural stability.

Conclusions:

• The identified Cu135@Au174 core-shell nanoalloy is a promising candidate for CO2 electrocatalysis.
• The developed correction scheme enables accurate and computationally feasible adsorption energy calculations.
• Copper incorporation stabilizes the icosahedral structure of gold nanoalloys, improving their catalytic potential.