Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network approach.

ABSTRACT

Hopfield neural network was used to retrieve liquid gallium radial distribution function from an experimental structure factor, obtained at 959 K. The inversion framework was carried out under two initial conditions (a) a constant radial distribution function corresponding to an ideal gas and (b) a step function, simulating a gas with square well potential of interaction. Both situations lead to accurate inverse results if compared with the radial distribution function obtained by Bellisent-Funel et al., using the Fourier transform method and Monte Carlo simulation. The Hopfield neural network has shown to be a powerful strategy to calculate the radial distribution function from experimental data.