5-step algorithm to accelerate the prediction of [Au₂₅(SR)₁₉] clusters (z = 1-, 0, 1+)

Area of Science:

• Computational Chemistry
• Materials Science
• Nanotechnology

Background:

• Predicting the structure of thiolated gold clusters is computationally intensive.
• Existing models for Au25(SR)18 clusters do not account for variations with additional ligands.

Purpose of the Study:

• To develop a faster method for predicting thiolated gold cluster structures.
• To propose new structural models for neutral and charged Au25(SR)19 clusters.

Main Methods:

• A five-step algorithm was employed.
• Dispersion-corrected density functional theory (DFT-D) calculations were utilized.
• The algorithm identifies tetrahedra/octahedra units and orders isomers by energy.

Main Results:

• A new, more stable structure for neutral Au25(SR)19 clusters was identified, 0.20 eV lower in energy than previously reported models.
• A common structure with an Au11 inner core was found to be the energy minimum for both neutral and charged Au25(SR)19 clusters.
• Ultraviolet-visible/circular dichroism profiles were similar for the new structures and those with C2v symmetry.

Conclusions:

• The developed algorithm and DFT-D approach effectively predict stable structures for thiolated gold clusters.
• The findings provide new insights into the structural diversity and stability of Au25(SR)19 clusters.
• This work offers a more efficient strategy for the structural prediction of complex gold nanoclusters.