Partial radial distribution functions for a two-component glassy solid, GeSe3, from scattering experimental data using an artificial intelligence framework

The Hopfield neural network has been applied successfully to solve ill-posed inverse problems in simple monoatomic liquids structure using scattering experimental data to retrieve the radial distribution function, g ( r ), and direct correlation function, C ( r ). In this work, the method was extended to a more complex system: a two-component glassy solid, GeSe 3 . To acquire results with correct peak intensities and behavior for large values of r , it was necessary to carry out the calculations a few times by adjusting the initial conditions to solve a set of coupled equations. However, the new initial conditions are simple and can be defined based on the results obtained at each run. In this sense, the method robustness is also evident while retrieving the radial distribution function for more complex systems from experimental data.