Paraphrasing Francis Crick: If you want to understand structure, study spectrum*

Tracking the structure of functional nanomaterials under operando conditions is a challenge due to the paucity of experimental techniques that can provide atomic-level information for active metal species. As a result, the search of activity descriptors relies almost exclusively on theoretical prediction and human expertise. X-ray absorption fine structure spectroscopy (XAFS) stands out as an element-specific method that is very sensitive to the local geometric, dynamic and electronic properties of the metal atoms and their surroundings and, is, therefore, able to track local structure modifications in operando conditions. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning for investigating the information content “hidden” in the spectra. Using an autoencoder-based approach, we zoom in on the latent space for obtaining the number of key descriptors that affect the spectrum. Using a multilayer perceptron, we determine the values of key descriptors in metal nanocatalysts and size-selective clusters. We demonstrate that these methods are superior with respect to commonly used extended X-ray absorption fine structure (EXAFS) analysis of local structural properties in many cases, notably when EXAFS data quality is limited by low catalyst loadings and harsh reaction conditions in operando experiments. In the both cases, we train the artificial neural network on theoretical XANES simulations and use it to “invert” the experimental spectrum and obtain the corresponding structural properties. Several applications of these methods to the determination of catalytic descriptors in operando conditions, such as studies of synthesis, nucleation, growth and reactivity of metal catalysts will be demonstrated.