Application of neural networks: a molecular geometry optimization study

Author

Lemes, M.R. ; Zacharias, C.R. ; Pino, A. Dal, Jr.

Author_Institution

Dept. de Fisica, ITA, Sao Paulo, Brazil

fYear

2000

fDate

2000

Firstpage

288

Abstract

Summary form only given. Optimization algorithms are iterative procedures that evolve from guessed starting points (SP) to the desired global minimum. Their performance can be greatly improved, if a neural network (NN) is created to select suitable SP. In this paper we consider the use of trained NN to select possible ground-state geometries for silicon clusters. A genetic algorithm is initial population energy optimization. For convenience, a cluster´s geometry is described as a piling up of plane layers of atoms

Keywords

atomic clusters; chemistry computing; iterative methods; minimisation; molecular configurations; neural nets; optimisation; silicon; GA; NN; Si; Si clusters; atom plane layers; cluster geometry; genetic algorithm; global minimum; guessed starting points; initial population energy optimization; iterative procedures; molecular geometry optimization study; neural networks; silicon cluster ground-state geometries; Atomic layer deposition; Clustering algorithms; Genetic algorithms; Genetic mutations; Geometry; Iterative algorithms; Neural networks; Optimization methods; Silicon; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on

Conference_Location

Rio de Janeiro, RJ

ISSN

1522-4899

Print_ISBN

0-7695-0856-1

Type

conf

DOI

10.1109/SBRN.2000.889760

Filename

889760