When: 25 March 2019, 1pm
Where: A1/3 Physics, UCL, Physics Building, Gower Street, London, WC1E 6BT
Tracking the structure of nanocatalysts (and other functional nanomaterials) under operating conditions is a challenge due to the paucity of experimental techniques that can provide atomic-level information for active metal species. Prof. Frenkel will demonstrate the use of X-ray absorption spectroscopy (XAS) and supervised machine learning (SML) for determining the three-dimensional geometry of metal catalysts at the sub-nanometer size scale. Artificial neural network is used to unravel the hidden relationship between the XAS features and catalyst geometry. In other words, they trained computer to learn how to ‘invert” the unknown spectrum and obtain the underlying structural descriptors. This method is demonstrated by reconstructing the average size, shape and morphology of nanoparticles with narrow size and composition distributions from the coordination numbers and interatomic distances obtained using the SML approach. First applications of this method to the determination of nanomaterial structure in operando conditions, such as studies of synthesis, nucleation, growth and reactivity of metal catalysts will be demonstrated.