Transition from exo to endo Cu absorption in CuSin clusters: A genetic algorithms density functional theory study
The characterisation and prediction of the structures of metal silicon clusters is important for nanotechnology research because these clusters can be used as building blocks for nanodevices, integrated circuits and solar cells. Several authors have postulated that there is a transition between exo...
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Taylor and Francis Ltd.
2011
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| 100 | 1 | |a Oña, O.B. | |
| 245 | 1 | 0 | |a Transition from exo to endo Cu absorption in CuSin clusters: A genetic algorithms density functional theory study |
| 260 | |b Taylor and Francis Ltd. |c 2011 | ||
| 270 | 1 | 0 | |m Facelli, J.C.; Department of Biomedical Informatics, University of Utah, 155 South 1452 East, Salt Lake City, UT, United States; email: julio.facelli@utah.edu |
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| 506 | |2 openaire |e Política editorial | ||
| 520 | 3 | |a The characterisation and prediction of the structures of metal silicon clusters is important for nanotechnology research because these clusters can be used as building blocks for nanodevices, integrated circuits and solar cells. Several authors have postulated that there is a transition between exo and endo absorption of Cu in Sin clusters and showed that, for n larger than 9, it is possible to find endohedral clusters. Unfortunately, no global searches have confirmed this observation, which is based on local optimisations of plausible structures. Here, we use parallel genetic algorithms (GAs), as implemented in our modified genetic algorithms (MGAC) software, directly coupled with density functional theory energy calculations to show that the global search of CuSin cluster structures does not find endohedral clusters for n8 but finds them for n10. © 2011 Taylor & Francis. |l eng | |
| 536 | |a Detalles de la financiación: Universidad de Buenos Aires | ||
| 536 | |a Detalles de la financiación: PHY080012N | ||
| 536 | |a Detalles de la financiación: 1S10RR017214-0 | ||
| 536 | |a Detalles de la financiación: This work has been partially supported by computer time allocations from the NSF TeraGrid PHY080012N and CHPC allocation on the Arches cluster partially funded by the NIH NCRR Grant No. 1S10RR017214-0. The software for this work used the GAlib genetic algorithm package, written by Matthew Wall at the Massachusetts Institute of Technology. MBF greatly acknowledges the financial support from the Universidad de Buenos Aires and the Argentinean CONICET. | ||
| 593 | |a Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina | ||
| 593 | |a Department of Biomedical Informatics, University of Utah, 155 South 1452 East, Salt Lake City, UT, United States | ||
| 593 | |a Center for High Performance Computing, University of Utah, 155 South 1452 East, Salt Lake City, UT, United States | ||
| 690 | 1 | 0 | |a COPPER-SILICON CLUSTERS |
| 690 | 1 | 0 | |a GENETIC ALGORITHMS |
| 690 | 1 | 0 | |a GLOBAL OPTIMISATION |
| 690 | 1 | 0 | |a DENSITY FUNCTIONAL THEORY |
| 690 | 1 | 0 | |a GENETIC ALGORITHMS |
| 690 | 1 | 0 | |a GLOBAL OPTIMIZATION |
| 690 | 1 | 0 | |a STRUCTURAL OPTIMIZATION |
| 690 | 1 | 0 | |a DENSITY FUNCTIONAL THEORY STUDIES |
| 690 | 1 | 0 | |a ENDOHEDRAL CLUSTERS |
| 690 | 1 | 0 | |a ENERGY CALCULATION |
| 690 | 1 | 0 | |a GLOBAL OPTIMISATION |
| 690 | 1 | 0 | |a MODIFIED GENETIC ALGORITHMS |
| 690 | 1 | 0 | |a NANOTECHNOLOGY RESEARCH |
| 690 | 1 | 0 | |a PARALLEL GENETIC ALGORITHMS |
| 690 | 1 | 0 | |a SILICON CLUSTERS |
| 690 | 1 | 0 | |a CLUSTERING ALGORITHMS |
| 700 | 1 | |a Ferraro, Marta Beatriz | |
| 700 | 1 | |a Facelli, J.C. | |
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