Department of Chemistry Seminar – Dr. Anatoly Frenkel
About the event
Anatoly Frenkel, Department of Materials Science and Chemical Engineering, Stony Brook University
Joint appointment: Division of Chemistry, Brookhaven National Laboratory
Host: Liane Moreau
Title: Decoding Reactive Structures in Nanocatalysts Hidden in their X-ray Absorption Spectra
Abstract: Detecting the active sites of nanocatalysts and obtaining their structural descriptors in reaction conditions is a major challenge. The main reason is the paucity of experimental techniques that can provide atomic-level information for metal species in harsh reaction conditions, such as high temperature and pressure, low metal loadings, and a realistic reactor environment. Here I will report on the use of X-ray absorption spectroscopy (XAS), enabled by machine learning, for determining the three-dimensional geometry of monometallic and alloy nanoparticles. Artificial neural network (NN), an example of a supervised machine learning approach, is used to unravel the hidden relationship between the XANES features and descriptors of material geometry and electronic properties. In other words, we trained a computer to learn how to ‘invert” the unknown spectrum and map it onto the underlying structural and electronic descriptors. Using an unsupervised machine learning approach, we bracketed the a priori unknown number of descriptors by the number of nodes in the latent space of the autoencoder. In both cases, neural network training was performed by using theoretical spectroscopy codes. These applications are demonstrated by reconstructing the geometric shapes and compositional distributions in nanocatalysts (nanoparticles and size-selective clusters) studied under in situ and operando conditions from their XAS spectra. I will discuss opportunities for applying these methods to other spectroscopies and other functional nanoscale systems.
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