Dong, H;
Butler, KT;
Matras, D;
Price, SWT;
Odarchenko, Y;
Khatry, R;
Thompson, A;
... Vamvakeros, A; + view all
(2021)
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data.
npj Computational Materials
, 7
(1)
, Article 74. 10.1038/s41524-021-00542-4.
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Abstract
We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO2-ZrO2/Al2O3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments.
| Type: | Article |
|---|---|
| Title: | A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41524-021-00542-4 |
| Publisher version: | https://doi.org/10.1038/s41524-021-00542-4 |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10128800 |
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