Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated heremore » 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.