Molecular force field parametrization using multi-objective evolutionary algorithms

Author

Mostaghim, S. ; Hoffmann, M. ; Konig, P.H. ; Frauenheim, Th. ; Teich, J.

Author_Institution

Dept. of Electr. Eng., Paderborn Univ., Germany

Volume

1

fYear

2004

fDate

19-23 June 2004

Firstpage

212

Abstract

We suggest a novel tool for the parametrization of molecular force fields by using multi-objective optimization algorithms with a new set of physically motivated objective functions. The new approach is validated in the parametrization of the bonded terms for the homologous series of primary alcohols. Multi-objective evolutionary algorithms (MOEAs) and particularly multi-objective particle swarm optimization (MOPSO) are applied. The results show that in this case MOPSO finds solutions with higher convergence than the MOEA method. Physical analysis of the results confirms the performance of the MOPSO method and the choice of objective functions.

Keywords

chemistry computing; convergence; evolutionary computation; molecular force constants; optimisation; physics computing; bonded term parametrization; convergence; homologous series; molecular force field parametrization; multiobjective evolutionary algorithms; multiobjective particle swarm optimization; physically motivated objective functions; primary alcohols; Bonding; Chemicals; Computer science; Evolutionary computation; Genetic algorithms; Iterative methods; Particle swarm optimization; Performance analysis; Physics; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2004. CEC2004. Congress on

Print_ISBN

0-7803-8515-2

Type

conf

DOI

10.1109/CEC.2004.1330859

Filename

1330859