A new optimization technique for artificial neural networks applied to prediction of force constants of large molecules

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.

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