Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom. However, the quantitative analysis of these spectra is not straightforward. 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. 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. This approach overcoms the problem of the systematic difference between theoretical and experimental spectra. 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. The methodology was successfully applied to experimental data for the multicomponent Fe:SiO2 system and reference iron compounds, demonstrating the high prediction quality for both the theoretical validation sets and experimental data.

Automated summary

X-ray absorption near-edge structure (XANES) spectra can be analyzed using machine learning algorithms to establish the relationship between intuitive descriptors of spectra and local atomic and electronic structures, providing a simple and fast tool for extracting structural parameters, successfully demonstrated high prediction quality on experimental data.