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Lookup NU author(s): Dr Tudur David, Kathryn Garside, Professor Thomas Penfold
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
X-ray absorption near-edge structure (XANES) spectroscopy is widely used across the natural sciences to obtain element specific atomic scale insight into the structure of matter. However, despite its increasing use owing to the proliferation of high-brilliance third- and fourth-generation light sources such as synchrotrons and X-ray free-electron lasers, decoding the wealth of information encoded within each spectra can sometimes be challenging and often requires detailed calculations. In this Article we introduce a supervised machine learning method which aims at directly extracting structural information from a XANES spectrum. Using a convolutional neural network, trained using theoretical data, our approach performs this direct translations of spectral information and achieves a median error in first coordination shell bond-lengths of 0.1 Å, when applied to experimental spectra. By combining this with the bootstrap resampling approach, our network is also able to quantify the uncertainty expected, providing non-experts with a metric for the reliability of each prediction. This work sets the foundation for future work in delivering techniques that can accurately quantify structural information directly from XANES spectra.
Author(s): David T, Aznan N, Garside K, Penfold TJ
Publication type: Article
Publication status: Published
Journal: Digital Discovery
Year: 2023
Volume: 2
Issue: 5
Pages: 1461-1470
Online publication date: 29/08/2023
Acceptance date: 29/08/2023
Date deposited: 29/08/2023
ISSN (electronic): 2635-098X
Publisher: Royal Society of Chemistry
URL: https://doi.org/10.1039/D3DD00101F
DOI: 10.1039/D3DD00101F
Data Access Statement: Data supporting this publication is openly available. The software can be obtained from gitlab.com/team-xnet/xanesnet, while the data can be obtained from gitlab.com/teamxnet/training-sets.
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