Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning

Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal sym...

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Autores principales: Leslie Ching Ow Tiong, Jeongrae Kim, Sang Soo Han, Donghun Kim
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Materials of engineering and construction. Mechanics of materials
TA401-492
Computer software
QA76.75-76.765
Acceso en línea:https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a
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spelling oai:doaj.org-article:0d364a8f4eba4550813da789ef5c508a2021-12-02T12:40:37ZIdentification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning10.1038/s41524-020-00466-52057-3960https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00466-5https://doaj.org/toc/2057-3960Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.Leslie Ching Ow TiongJeongrae KimSang Soo HanDonghun KimNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
institution DOAJ
collection DOAJ
language EN
topic 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
Leslie Ching Ow Tiong
Jeongrae Kim
Sang Soo Han
Donghun Kim
Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
description Abstract The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry. Despite their promise, most of these studies have been limited to identifying relatively few classes into which a target material may be grouped. On the other hand, the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes (e.g., up to 230 space groups), severely limiting its practical usage. Here, we demonstrate that a combined approach of shaping diffraction patterns and implementing them in a multistream DenseNet (MSDN) substantially improves the accuracy of classification. Even with an imbalanced dataset of 108,658 individual crystals sampled from 72 space groups, our model achieves 80.12 ± 0.09% space group classification accuracy, outperforming conventional benchmark models by 17–27 percentage points (%p). The enhancement can be largely attributed to the pattern shaping strategy, through which the subtle changes in patterns between symmetrically close crystal systems (e.g., monoclinic vs. orthorhombic or trigonal vs. hexagonal) are well differentiated. We additionally find that the MSDN architecture is advantageous for capturing patterns in a richer but less redundant manner relative to conventional convolutional neural networks. The proposed protocols in regard to both input descriptor processing and DL architecture enable accurate space group classification and thus improve the practical usage of the DL approach in crystal symmetry identification.
format article
author Leslie Ching Ow Tiong
Jeongrae Kim
Sang Soo Han
Donghun Kim
author_facet Leslie Ching Ow Tiong
Jeongrae Kim
Sang Soo Han
Donghun Kim
author_sort Leslie Ching Ow Tiong
title Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
title_short Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
title_full Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
title_fullStr Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
title_full_unstemmed Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
title_sort identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/0d364a8f4eba4550813da789ef5c508a
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AT jeongraekim identificationofcrystalsymmetryfromnoisydiffractionpatternsbyashapeanalysisanddeeplearning
AT sangsoohan identificationofcrystalsymmetryfromnoisydiffractionpatternsbyashapeanalysisanddeeplearning
AT donghunkim identificationofcrystalsymmetryfromnoisydiffractionpatternsbyashapeanalysisanddeeplearning
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