The X-ray absorption near-edge structure (XANES), spanning a region of 50–200 eV above the absorption edge, contains information about the structural descriptors involving the bond distances and angles, type of ligand surrounding, oxidation state, which affect the spectral descriptors edge position, shapes and positions of spectral maxima and minima. X-ray absorption spectroscopy is widely employed to probe the local atomic and electronic structure around the absorbing atom 1, 2, 3. The methodology was successfully applied to experimental data for the multicomponent Fe:SiO 2 system and reference iron compounds, demonstrating the high prediction quality for both the theoretical validation sets and experimental data. Furthermore, the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape. This approach overcoms the problem of the systematic difference between theoretical and experimental spectra. Here, we present an alternative concept for the analysis of XANES spectra, which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra, such as edge position, intensities, positions, and curvatures of minima and maxima on the one hand, and those related to the local atomic and electronic structure which are the coordination numbers, bond distances and angles and oxidation state on the other hand. Even with the most recent advances in this area, for a given spectrum, it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated. However, the quantitative analysis of these spectra is not straightforward. X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom.
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