Abstract
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.
Highlights
Machine Learning (ML) methods are becoming widely used to tackle different scientific problems [1,2]
Various studies have been conducted to the use of those methods connected to X-ray absorption spectroscopy (XAS) [3,4,5,6,7,8,9,10,11,12,13,14,15]
The spectra is commonly divided into two regions, due to both experimental and theoretical reasons, depending on the energy range we consider: X-ray Absorption Near Edge Structure (XANES) for the near-edge region and Extended X-ray
Summary
Machine Learning (ML) methods are becoming widely used to tackle different scientific problems [1,2]. This popularity comes thanks to the advancement in the field and the success of the results, and due to the availability of various open-source packages, which make them accessible even to non-expert users. The spectra is commonly divided into two regions, due to both experimental and theoretical reasons, depending on the energy range we consider: X-ray Absorption Near Edge Structure (XANES) for the near-edge region (up to about 50 eV after the edge) and Extended X-ray. One the main advantages of XAS is that it does not require sample crystallinity, meaning it can be used to study a vast range of materials, such as molecules [18,19,20], amorphous systems [21,22,23,24]
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