Hopfield neural network model for calculating the potential energy function from second virial data

The calculation of the intermolecular potential from the second virial coef ficient is treated here by using a Hopfield neural network model. From simu lated data for the prototype system HeNe, the repulsive potential was obtai ned with a desired accuracy. The algorithm used here is general, as it can handle noise in the experimental data and, a neural network of higher dimen sion can be easily constructed. Although the inversion of the short-range p art of the potential was obtained in the present work, the Hopfield neural network under consideration can equally be used to invert virial data to gi ve the long-range part of the potential. The convergence of the states of t he neuron and the accuracy of the inverted potential is also discussed. (C) 2000 Elsevier Science B.V. All rights reserved.