Title :
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
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;
Conference_Titel :
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location :
Rio de Janeiro, RJ
Print_ISBN :
0-7695-0856-1
DOI :
10.1109/SBRN.2000.889760
