Genetic Algorithm Application to the Structural Properties of Si-Ge Mixed Clusters

2009-01-01
Dugan, Nazim
Erkoç, Şakir
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.
MATERIALS AND MANUFACTURING PROCESSES

Suggestions

Application of genetic algorithms to geometry optimization of microclusters: A comparative study of empirical potential energy functions for silicon
Erkoc, S; Leblebicioğlu, Mehmet Kemal; Halıcı, Uğur (Informa UK Limited, 2003-01-01)
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.
Genetic algorithms applied to Li+ ions contained in carbon nanotubes: An investigation using particle swarm optimization and differential evolution along with molecular dynamics
Chakraborti, N.; Das, S.; Jayakanth, R.; Pekoz, R.; Erkoç, Şakir (Informa UK Limited, 2007-01-01)
Empirical potentials based upon two and three body interactions were applied to the Li+ -C system, assuming the Li+ ions to be distributed inside high-symmetry, single walled carbon nanotubes of different chirality. Structural optimizations for various assemblages were conducted using evolutionary and genetic algorithms, where differential evolution and particle swarm optimization techniques worked satisfactorily. The results were compared with the outcome of some rigorous molecular dynamics simulations. Th...
Genetic algorithm-Monte Carlo hybrid geometry optimization method for atomic clusters
Dugan, Nazim; Erkoç, Şakir (Elsevier BV, 2009-03-01)
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 resul...
Multi-scale characterization of particle clustering in discontinuously reinforced composites
CETIN, Arda; Kalkanlı, Ali (Elsevier BV, 2009-06-01)
The applicability of a quantitative characterization scheme for cluster detection in particle reinforced composites is discussed. The method considers the pattern from the perspective of individual particles, so that even in a pattern that globally conforms to a random distribution, micro-scale heterogeneities can be detected. The detected clusters are visualized by kernel surfaces. Results indicate that the presented methodology is an effective discriminator of clusters and can successfully be used for qua...
Finding highly preferred points for multi-objective integer programs
LOKMAN, BANU; Köksalan, Mustafa Murat (Informa UK Limited, 2014-01-01)
This article develops exact algorithms to generate all non-dominated points in a specified region of the criteria space in Multi-Objective Integer Programs (MOIPs). Typically, there are too many non-dominated points in large MOIPs and it is not practical to generate them all. Therefore, the problem of generating non-dominated points in the preferred region of the decision-maker is addressed. To define the preferred region, the non-dominated set is approximated using a hyper-surface. A procedure is developed...
Citation Formats
N. Dugan and Ş. Erkoç, “Genetic Algorithm Application to the Structural Properties of Si-Ge Mixed Clusters,” MATERIALS AND MANUFACTURING PROCESSES, pp. 250–254, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56482.