Application of genetic algorithms to geometry optimization of microclusters: A comparative study of empirical potential energy functions for silicon

Evolutionary computation techniques (in particular, genetic algorithms) have been applied to optimize the structure of microclusters. Various empirical potential energy functions have been used to describe the interactions among the atoms in the clusters. A comparative study of silicon microclusters has been performed.


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Optimum geometries of silicon-germanium (Si-Ge) clusters are found using a single parent genetic algorithm. 100 atom and 150 atom clusters are studied with some variety of compositions and initial geometries. Total interaction energies, distances of Si and Ge atoms to the cluster centers, and average bond lengths are calculated. Si-core Ge-shell geometry is found to be favorable compared to other geometries.
ESKICIOGLU, AM; ESKICIOGLU, H (SAGE Publications, 1992-01-01)
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This thesis proposes a framework for determining the optimum location of an injection well by using an inference method, Artificial Neural Networks and a search algorithm to create a search space and locate the global maxima. Theoretical foundation of the proposed framework is followed by description of the field for case study. A complex carbonate reservoir, having a recorded geothermal production history is used to evaluate the proposed framework ( Kizildere Geothermal field, Turkey). In the proposed fram...
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Citation Formats
S. Erkoc, M. K. Leblebicioğlu, and U. Halıcı, “Application of genetic algorithms to geometry optimization of microclusters: A comparative study of empirical potential energy functions for silicon,” MATERIALS AND MANUFACTURING PROCESSES, pp. 329–339, 2003, Accessed: 00, 2020. [Online]. Available: