ISSN:
1434-6079
Keywords:
PACS: 61.46.+w Clusters, nanoparticles, and nanocrystalline materials – 36.40.Cg Electronic and magnetic properties of clusters – 02.60.Pn Numerical optimization
Source:
Springer Online Journal Archives 1860-2000
Topics:
Physics
Notes:
Abstract. Genetic algorithms (GA) are applied for the optimization of the structure of metallic clusters by the calculation of the ground-state energies from a tight-binding (Hückel) Hamiltonian. The optimum topology or graph is searched by the use of the adjacency matrix A ij as a natural coding. The initial populations for N-atom clusters are generated from a representative group of fit cluster structures having N-1 atoms by the addition of random connections or hoppings between the Nth atom and the rest of the cluster atoms (A iN =0 or 1). The diversity of geometries is enlarged by 20% with fully random structures. Several crossover strategies are proposed for the genetic evolution that combine the “parent” clusters while trying to preserve or transmit the physical characteristics of the parents’ topologies. The performance of the different procedures is tested. For N≤13, the present GA yield topological structures that are in agreement with previous geometry optimizations performed using an enumerative search (N≤9) or simulated annealing Monte Carlo (10≤N≤13) methods. Limitations and extensions for N≥14 are discussed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/PL00010925
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