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

The calculation of the intermolecular potential from the second virial coefficient is treated here by using a Hopfield neural network model. From simulated data for the prototype system HeNe, the repulsive potential was obtained 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 dimension can be easily constructed. Although the inversion of the short-range part of the potential was obtained in the present work, the Hopfield neural network under consideration can equally be used to invert virial data to give the long-range part of the potential. The convergence of the states of the neuron and the accuracy of the inverted potential is also discussed.