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Genetic algorithm-Monte Carlo hybrid geometry optimization method for atomic clusters
Date
2009-03-01
Author
Dugan, Nazim
Erkoç, Şakir
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In this work, an evolutionary type global optimization method for identifying the stable geometries of atomic clusters is developed and applied to carbon clusters for testing purpose. Monte Carlo (MC) type local optimization is used between genetic algorithm (GA) steps together with a special Mutation operation designed for the Cluster geometry optimization problem. Cluster geometries and the corresponding potential energies for carbon obtained with this GA-MC hybrid method are compared with available results in the literature and reliability of the method is justified for moderate sized carbon clusters.
Subject Keywords
General Physics and Astronomy
,
General Materials Science
,
General Computer Science
,
Mechanics of Materials
,
General Chemistry
,
Computational Mathematics
URI
https://hdl.handle.net/11511/57191
Journal
COMPUTATIONAL MATERIALS SCIENCE
DOI
https://doi.org/10.1016/j.commatsci.2008.03.045
Collections
Department of Physics, Article
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N. Dugan and Ş. Erkoç, “Genetic algorithm-Monte Carlo hybrid geometry optimization method for atomic clusters,”
COMPUTATIONAL MATERIALS SCIENCE
, pp. 127–132, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57191.