Extracting Local Symmetry of Mono-Atomic Systems from Extended X-ray Absorption Fine Structure Using Deep Neural Networks

F Iesari and H Setoyama and T Okajima, SYMMETRY-BASEL, 13, 1070 (2021).

DOI: 10.3390/sym13061070

In recent years, neural networks have become a new method for the analysis of extended X-ray absorption fine structure data. Due to its sensitivity to local structure, X-ray absorption spectroscopy is often used to study disordered systems and one of its more interesting property is the sensitivity not only to pair distribution function, but also to three-body distribution, which contains information on the local symmetry. In this study, by considering the case of Ni, we show that by using neural networks, it is possible to obtain not only the radial distribution function, but also the bond angle distribution between the first nearest-neighbors. Additionally, by adding appropriate configurations in the dataset used for training, we show that the neural network is able to analyze also data from disordered phases (liquid and undercooled state), detecting small changes in the local ordering compatible with results obtained through other methods.

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