Computational Intelligence-Based Parametrization on Force-Field Modeling for Silicon Cluster Using ASBO
A new parametrization of the small-size silicon cluster is proposed in this paper to improve the quality of predicted energy value by potential energy function in force-field modeling. ASBO-based concept has applied to evolve the parameters under different circumstances and cluster structure. The performance of new parameters is compared with the other well-established parameters in stillinger–weber energy function and its variants. Under known and unknown environment, effects of higher dimension in energy predicting capability are also analyzed. A significant improvement is observed in predicting the small cluster energy value with a proposed solution compared to values obtained with existing parameters. PSO with dynamic weight (DWPSO) is also applied to analyze the comparative capability of ASBO in solution exploration and convergence characteristics, and there is a remarkable improvement observed with ASBO-based solution.
KeywordsNanotechnology Molecular force field Potential energy function Interatomic interaction Computational intelligence ASBO PSO
This research has completed in Manuro Tech Research Pvt. Ltd., Bangalore, India. The Authors express their thanks to Mrs. Reeta Kumari (Director) for her valuable suggestion to accomplish this research.
- 3.Stillinger, W.: Computer simulation of local order in condensed phase of silicon. Phys. Rev. 31(8) (1985)Google Scholar
- 4.Gong, X.G., Zheng, Q.Q., He, Y.-Z.: Structural properties of silicon clusters: an empirical potential study. J. Phys. Condens. Matter 7 (1995)Google Scholar
- 5.Globus, A., Menon, M., Ricks, E., Srivastava, D.: Evolving molecular force field parameters for Si and Ge. NSTI Nanotechnol. Conf. Trade Show (2003)Google Scholar
- 7.Pizzagalli, L.: A new parametrization of the Stillinger–Weber potential for an improved description of defects and plasticity of silicon. J. Phys. Condens. Matter 25(5) (2013)Google Scholar
- 8.Mostaghim, S.: Molecular force field parametrization using multi-objective evolutionary algorithms. IEEE, CEC. 1 (2004)Google Scholar
- 10.Larsson, H.R., Hartke, B.: Fitting reactive force fields using genetic algorithms. Comput. Method Mater. Sci. 13(1) (2013)Google Scholar
- 14.Baturin, V.S.: Structural and electronic properties of small silicon clusters. J. Phys. Conf. Series 510 (2014)Google Scholar
- 15.Singh, M.K.: A new optimization method based on adaptive social behavior: ASBO. Springer AISC 174, 823–831 (2012)Google Scholar
- 16.Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1) (2002)Google Scholar