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X-ray absorption spectroscopy at the L-edge probes transitions of2p-electrons into unoccupied d-states. Applied to transition metal atoms, thisexperimental technique can provide valuable information about the electronicstructure of d-states. However, multiplet effects, spin-orbit coupling, a largenumber of possible transitions can cause a rather involved nature of 2p XASspectra, which can often complicate extracting of information directly fromthem. Here, artificial neural networks trained on simulated spectra of a 2p XASmodel Hamiltonian are presented that can directly determine information aboutatomic properties and the electronic configuration of d-states from L-edgeX-ray absorption spectra. Moreover, the adaptable nature of artificial neuralnetworks (ANNs) allows extending their capability to obtain information aboutthe electronic ground state and core hole lifetimes from 2p XAS spectra as wellas to incorporate external factors, such as temperature and experimentalconvolution that can affect details in spectral features. The effects of noiseand background contributions in spectra on the accuracy of ANNs are discussedand the method is validated on experimental spectra of transition metalcompounds, including metal-organic molecules and metal oxides.

Determining electronic properties from L-edge X-ray absorption spectra of transition metal compounds with artificial neural networks