  X-ray absorption spectroscopy at the L-edge probes transitions of
2p-electrons into unoccupied d-states. Applied to transition metal atoms, this
experimental technique can provide valuable information about the electronic
structure of d-states. However, multiplet effects, spin-orbit coupling, a large
number of possible transitions can cause a rather involved nature of 2p XAS
spectra, which can often complicate extracting of information directly from
them. Here, artificial neural networks trained on simulated spectra of a 2p XAS
model Hamiltonian are presented that can directly determine information about
atomic properties and the electronic configuration of d-states from L-edge
X-ray absorption spectra. Moreover, the adaptable nature of artificial neural
networks (ANNs) allows extending their capability to obtain information about
the electronic ground state and core hole lifetimes from 2p XAS spectra as well
as to incorporate external factors, such as temperature and experimental
convolution that can affect details in spectral features. The effects of noise
and background contributions in spectra on the accuracy of ANNs are discussed
and the method is validated on experimental spectra of transition metal
compounds, including metal-organic molecules and metal oxides.
