Ultradispersed catalysts – subnanometer-sized nanoparticles (NPs) and single atom catalysts – are attracting attention due to their enhanced catalytic performance. One of the limitations of such materials is that the nature of active sites is still mostly unknown due to the scarce information, provided by experimental techniques. X-ray absorption spectroscopy (XAS) is one of a few methods that are useful in this case, due to its sensitivity to the chemical state of absorbing atoms and to the types and arrangements of atoms around the absorber. Importantly, XAS method also allows in-operando investigations. While the extended X-ray absorption fine structure (EXAFS) has been used for decades in studies of nanocatalysts, much less attention has been paid to the information encoded in X-ray absorption near edge structure (XANES). At the same time, it is acknowledged that XANES is less prone to the disorder effects than EXAFS, and is also more sensitive to the 3D arrangements of atoms around the absorbing metal centers. The main challenge that hinders the usage of XANES for quantitative analysis is the lack of methodology that would allow one to extract such 3D structural information from experimental data. Recent advances in data-enabled discovery science methods in chemical research provide a key to this problem. In this study for the first time we employ machine learning methods and ab-initio XANES calculations to correlate XANES features with the descriptors of 3D local structure of ultradispersed catalyst. Here we demonstrate the potentiality of this method on the example of XANES study of small supported platinum NPs. We use theoretical site-specific XANES, calculated by ab-initio codes, to train an artificial neural network (NN). The trained NN is then used to extract structural information from the experimental XANES data, and to determine the size and shape of investigated metallic NPs.

Machine learning guided interpretation of X-ray absorption data for ultradispersed catalysts

254th National Meeting (2017)

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