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Global Optimization of Atomic Cluster Structures Using Parallel Genetic Algorithms

Published online by Cambridge University Press:  26 February 2011

Ofelia Oña,
Victor E. Bazterra,
María C. Caputo,
Marta B. Ferraro and
Julio Facelli
Ofelia Oña
Affiliation:
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. I (1428), Buenos Aires, Argentina
Victor E. Bazterra
Affiliation:
Center for High Performance Computing, University of Utah, 155 South 1452 East Rm 405, Salt Lake City, UT 84112-0190
María C. Caputo
Affiliation:
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. I (1428), Buenos Aires, Argentina
Marta B. Ferraro
Affiliation:
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. I (1428), Buenos Aires, Argentina
Julio Facelli
Affiliation:
Julio.Facelli@utah.edu, University of Utah, CTR FOR HIGH PERFORMANCE COMPUTING, 155 SOUTH 1452 EAST RM 40, SALT LAKE CITY, Utah, 84112-0190, United States
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Abstract

The study of the structure and physical properties of atomic clusters is an extremely active area of research due to their importance, both in fundamental science and in applied technology. For medium size atomic clusters most of the structures reported today have been obtained by local optimizations of plausible structures using DFT (Density Functional Theory) methods and/or by global optimizations in which much more approximate methods are used to calculate the cluster’s energetics. Our previous work shows that these approaches can not be reliably used to study atomic cluster structures and that approaches based on global optimization schemes are needed. In this paper, we report the implementation and application of a parallel Genetic Algorithm (GA) to predict the structure of medium size atomic clusters.

Keywords

simulationcluster assemblySi

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References

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