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Updated: May 30, 2026
12:11
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
Transition from exo- to endo- Cu absorption in CuSi(n) clusters: A Genetic Algorithms Density Functional Theory (DFT)
Ofelia B Oña1, Marta B Ferraro, Julio C Facelli
1Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina, ofelia@df.uba.ar ; ferraro@df.uba.ar .
Molecular Simulation
|July 26, 2011
Summary
Researchers explored copper in silicon clusters using genetic algorithms. Endohedral structures were not found for fewer than 8 silicon atoms, but were confirmed for 10 or more silicon atoms.
Area of Science:
- Computational materials science
- Nanotechnology
- Quantum chemistry
Background:
- Metal silicon clusters are crucial building blocks in nanotechnology for devices and solar cells.
- Previous studies suggested a transition to endohedral copper absorption in silicon clusters for n > 9, based on local optimizations.
- A comprehensive global search was lacking to confirm these findings.
Purpose of the Study:
- To rigorously characterize and predict the structures of copper-silicon (CuSi(n)) clusters.
- To investigate the transition point for endohedral copper absorption in silicon clusters.
- To confirm or refute previous hypotheses regarding cluster structures using global optimization.
Main Methods:
- Utilized parallel Genetic Algorithms (GA) for global structure searching.
- Integrated Density Functional Theory (DFT) calculations for energy evaluations.
- Employed custom software (MGAC) for direct coupling of GA and DFT.
Main Results:
- Global structure searches confirmed endohedral CuSi(n) clusters are not found for n < 8.
- Endohedral CuSi(n) cluster structures were identified for n ≥ 10.
- The study provides definitive evidence on the size-dependent structural transitions in CuSi(n) clusters.
Conclusions:
- The transition to endohedral copper absorption in silicon clusters occurs at n=10.
- This research clarifies the structural landscape of CuSi(n) clusters, impacting nano device design.
- The findings validate the use of GA coupled with DFT for complex cluster structure prediction.
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