A deep convolutional neural network for real-time full profile analysis of big powder diffraction data
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...
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Nature Portfolio
2021
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oai:doaj.org-article:7adac6f0b28a45c1aa8cd163d3fb007b2021-12-02T15:45:30ZA deep convolutional neural network for real-time full profile analysis of big powder diffraction data10.1038/s41524-021-00542-42057-3960https://doaj.org/article/7adac6f0b28a45c1aa8cd163d3fb007b2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00542-4https://doaj.org/toc/2057-3960Abstract 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.Hongyang DongKeith T. ButlerDorota MatrasStephen W. T. PriceYaroslav OdarchenkoRahul KhatryAndrew ThompsonVesna MiddelkoopSimon D. M. JacquesAndrew M. BealeAntonis VamvakerosNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (2021) |
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DOAJ |
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EN |
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Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 |
| spellingShingle |
Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Hongyang Dong Keith T. Butler Dorota Matras Stephen W. T. Price Yaroslav Odarchenko Rahul Khatry Andrew Thompson Vesna Middelkoop Simon D. M. Jacques Andrew M. Beale Antonis Vamvakeros A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| description |
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. |
| format |
article |
| author |
Hongyang Dong Keith T. Butler Dorota Matras Stephen W. T. Price Yaroslav Odarchenko Rahul Khatry Andrew Thompson Vesna Middelkoop Simon D. M. Jacques Andrew M. Beale Antonis Vamvakeros |
| author_facet |
Hongyang Dong Keith T. Butler Dorota Matras Stephen W. T. Price Yaroslav Odarchenko Rahul Khatry Andrew Thompson Vesna Middelkoop Simon D. M. Jacques Andrew M. Beale Antonis Vamvakeros |
| author_sort |
Hongyang Dong |
| title |
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| title_short |
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| title_full |
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| title_fullStr |
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| title_full_unstemmed |
A deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| title_sort |
deep convolutional neural network for real-time full profile analysis of big powder diffraction data |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/7adac6f0b28a45c1aa8cd163d3fb007b |
| work_keys_str_mv |
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