  We explore the applicability of artificial intelligence for molecular
structure - core-level spectrum interpretation. We focus on the electronic
Hamiltonian using the H$_2$O molecule in the classical-nuclei approximation as
our test system. For a systematic view we studied both predicting structures
from spectra and, vice versa, spectra from structures, using polynomial
approaches and neural networks. We find predicting spectra easier than
predicting structures, where a tighter grid of the spectrum improves
prediction. However, the accuracy of the structure prediction worsens when
moving outwards from the center of mass of the training set in the structural
parameter space.
