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A new optimization technique for artificial neural networks applied to prediction of force constants of large molecules
Author(s) -
Fischer Thomas H.,
Petersen Wesley P.,
Lüthi Hans Peter
Publication year - 1995
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540160802
Subject(s) - hessian matrix , artificial neural network , force constant , simulated annealing , computer science , polyatomic ion , molecule , constant (computer programming) , computational chemistry , statistical physics , artificial intelligence , chemistry , algorithm , mathematics , physics , quantum mechanics , programming language
An artificial neural network (ANN) method for the prediction of force constants of chemical bonds in large, polyatomic molecules was developed. The force constant information evaluated is to be used for generating accurate estimates of the Hessian used in Newton‐Raphson‐type ab initio molecular structure optimization schemes. Different network topologies as well as a training procedure based on simulated annealing are evaluated. The results show that an ANN can be designed and trained to provide force constant information within a 1.5 to 5% error band even if the range of the force constants evaluated is very large (from triple bonds to hydrogen bridges). © 1995 by John Wiley & Sons, Inc.