Representation of intermolecular potential functions by neural networks

We have investigated how a neural network representation of intermolecular potential functions can be used to elevate some of the problems commonly encountered during fitting and application of analytical potential functions in computer simulations. For this purpose we applied feed-forward networks of various sizes to reproduce the three-body interaction energies in the system H(2)O-Al(3+)-H(2)O. In this highly polarizable system the three-body interaction terms are necessary for an accurate description of the system, and it proved difficult to fit an analytical function to them. Subsequently we performed Monte Carlo simulations on an Al(3+ )ion dissolved in water and compared the results obtained using the neural network type potential function with those using a conventional analytical potential. The performance and results of our calculations lead to the conclusion that, for suitable systems, the advantages of a neural network type representation of potential functions as a model-independent and "semiautomatic" potential function outweigh the disadvantages in computing speed and lack of interpretability.