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Simulation of spectra of x-ray absorption spectroscopy (XAS) at the L-edge isa well-established and reliable computational tool that, in combination withexperimental measurements, reveals details about the local electronic structureand the chemical environment of transition metal ions (TMI). For light TMIs,model 2p XAS model Hamiltonian approaches (MHA) are fast and accurate incomputing spectra. Here, an alternative method using artificial neural networks(ANN) is employed. The new approach predicts x-ray absorption spectra at theL-edge of light transition metals ions for elements from Ti to Ni, includingdifferent oxidation states, from sets of physical input parameters.Computational performance and accuracy were optimized through a systematicexploration of ANN design and compared to MHA simulations. While the MHAremains more accurate due to the statistical nature of the developed machinelearning method, the ANN-based prediction of 2p XAS spectra achieved goodaccuracy of more than 99%, in addition to a noticeable advantage incomputational speed. This makes the method intrinsically more suitable forcomputational high-throughput setups as well as in support of data-intenseexperimental workflows. However, the extrapolation weakness of artificialneural networks remained for most cases and displayed a strong dependence onthe TMI. For one TMI, an extrapolation accuracy of 78% was maintained.

A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds