X-ray Absorption Near Edge Structure (XANES) is well-adapted for in situ and operando experiments. It is both atomically specific and it encodes local structure of the surrounding atoms. Due to multiple scattering effects, inferring that structure from the spectra can be complex. In the Center for Functional Nanomaterials, we pursue several prototype projects with collaborating groups [1] to explore approaches to solve this inverse problem. We seek to go beyond the empirical fingerprint method, particularly to infer structure motifs that emerge in studies of new or nanostructured materials. Our over-arching approach is two-fold: exploit direct computation of XANES spectra for a pertinent database of material structures to support and train data analysis techniques; validate through comparison to experiments. We use a set of first-principles computational tools (FEFF, XSPECTRA, OCEAN, EXCITING) that span a range of computational complexity, theoretical approach and accuracy. We draw on publicly available crystal structure databases and develop structural models for more complex systems, e.g., nanoparticles and amorphous solids, using density functional theory based techniques. Three prototype projects will be described. First, simulated spectra for a database of Pt nanoparticle structures was used to train a neural network to extract average structure descriptors based on coordination numbers out to the fourth neighbor shell [2]. Validation demonstrated inference of particle size and shape. Second, a large database of simulated cation K-edge spectra was developed for oxides in eight 3d transition metal families and then used with machine learning to classify according to the local coordination environment (4-, 5- and 6-fold) [3]. Third, spectral modeling was used to infer Ti coordination in ultra-thin titania coatings grown conformally over ZnO nanowires. Our outlook for future developments and the potential for impact on operando experimentation will be discussed.

Structure inference from X-ray absorption spectroscopy for operando experimentation

2019 Fall National Meeting

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