Global geometry optimization of silicon clusters using the space-fixed genetic algorithm
β Scribed by Iwamatsu, Masao
- Book ID
- 121835048
- Publisher
- American Institute of Physics
- Year
- 2000
- Tongue
- English
- Weight
- 655 KB
- Volume
- 112
- Category
- Article
- ISSN
- 0021-9606
- DOI
- 10.1063/1.481737
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A modified genetic algorithm approach has been applied to Ε½ . atomic Ar clusters and molecular water clusters up to H O . Several genetic 2 13 operators are discussed which are suitable for real-valued space-fixed atomic coordinates and Euler angles. The performance of these operators has been syst
A space-fixed modified genetic algorithm (SFMGA) approach was used to obtain global minima for the silicon clusters (Si) n using a semiempirical potential. One modification to the usual GA is the use of gradient-driven minimization of each geometry. A novel feature of the method is the use of space-
A new strategy for global geometry optimization of clusters is presented. Important features are a restriction of search space to favorable nearest-neighbor distance ranges, a suitable cluster growth representation with diminished correlations, and easy transferability of the results to larger clust
## Abstract A modified genetic algorithm with realβnumber coding, nonβuniform mutation and arithmetical crossover operators was described in this paper. A local minimization was used to improve the final solution obtained by the genetic algorithm. Using the expβ6β1 interatomic energy function, the