Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426
Jan Kloppenburg, Andreas Pedersen, Kari Laasonen, Miguel A. Caro,, Hannes J\'onsson

TL;DR
This paper redefines the magic numbers for stable icosahedral gold clusters by optimizing their structure with advanced computational methods, revealing more stable configurations with different atom counts than traditionally assumed.
Contribution
The study introduces new stable icosahedral gold cluster structures with revised magic numbers, using combined empirical, DFT, and GAP methods for structural optimization.
Findings
Identified more stable cluster sizes at 310, 564, 928, and 1426 atoms.
Revealed structural features like hexagonal rings and atom displacements affecting catalysis.
Discovered a single energy barrier between Mackay and lower energy structures.
Abstract
Icosahedral Au clusters with three and four shells of atoms are found to deviate significantly from the commonly assumed Mackay structures. By introducing additional atoms in the surface shell and creating a vacancy in the center of the cluster, the calculated energy per atom can be lowered significantly, according to several different descriptions of the interatomic interaction. Analogous icosahedral structures with five and six shells of atoms are generated using the same structural motifs and are similarly found to be more stable than Mackay icosahedra. The lowest energy per atom is obtained with clusters containing 310, 564, 928 and 1426 atoms, as compared with the commonly assumed magic numbers of 309, 561, 923 and 1415. Some of the vertices in the optimized clusters have a hexagonal ring of atoms, rather than a pentagon, with the vertex atom missing. An inner shell atom in some…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Chemical Physics Studies · Machine Learning in Materials Science · Catalytic Processes in Materials Science
