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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Citation Formats

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.