ArticleChemical science2026
Guiding blind signal separation in X-ray absorption spectroscopy using supervised machine learning.
Article in Chemical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Transformations under working conditions experienced by heterogeneous catalysts and other functional materials commonly result in complex, time-dependent atomistic structures, where the active species that are relevant for the functionality of the material coexist with passive spectators. Decoupling the contributions of multiple coexisting species to spectroscopic data remains a common challenge for many sample-averaging techniques, including X-ray absorption spectroscopy. Here we demonstrate that a data-driven, supervised machine learning based approach can greatly assist in the challenging task of singling-out the contributions of spectroscopically distinct species. This is accomplished by recognizing common patterns in large datasets of X-ray absorption spectra that are calculated
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