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....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:SiO_(2)system and reference iron compounds,demonstrating the high prediction quality for both the theoretical validation sets and experimental data.展开更多
基金A.Guda acknowledges the financial support from the Russian Foundation for Basic Research(project number 20-32-70227)for the work on the multicomponent mixtures.A.Bugaev and A.V.Soldatov acknowledge the Russian Science Foundation grant#20-43-01015 for the financial support for the work on the spectral descriptors.Authors acknowledge D.D.Badyukov from Vernadsky Institute of Geochemistry and Analytical Chemistry of Russian Academy of Sciences for providing samples for analysis.P.Šot acknowledges the Shell Global Solutions International,B.V.for funding the work on the synthesis of Fe-containing catalyst,and European Synchrotron Research Facility for awarded beamtimes at beamlines ID26,BM25,and Swiss Light Source for the beamtime at SuperXAS beamline.
文摘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:SiO_(2)system and reference iron compounds,demonstrating the high prediction quality for both the theoretical validation sets and experimental data.
