This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated heremore ยป by reconstructing the average size, shape and morphology of well-defined platinum nanoparticles. To train our SML method, we rely on ab-initio XANES simulations. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the three-dimensional geometry of metal catalysts. Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species.
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