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Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic struct...

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Autores principales: Yanxon, Howard, Weng, James, Parraga, Hannah, Xu, Wenqian, Ruett, Uta, Schwarz, Nicholas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814056/
https://www.ncbi.nlm.nih.gov/pubmed/36601933
http://dx.doi.org/10.1107/S1600577522011274
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author Yanxon, Howard
Weng, James
Parraga, Hannah
Xu, Wenqian
Ruett, Uta
Schwarz, Nicholas
author_facet Yanxon, Howard
Weng, James
Parraga, Hannah
Xu, Wenqian
Ruett, Uta
Schwarz, Nicholas
author_sort Yanxon, Howard
collection PubMed
description In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions (e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.
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spelling pubmed-98140562023-01-09 Artifact identification in X-ray diffraction data using machine learning methods Yanxon, Howard Weng, James Parraga, Hannah Xu, Wenqian Ruett, Uta Schwarz, Nicholas J Synchrotron Radiat Research Papers In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g. battery materials) or in complex sample environments (e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions (e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method. International Union of Crystallography 2023-01-01 /pmc/articles/PMC9814056/ /pubmed/36601933 http://dx.doi.org/10.1107/S1600577522011274 Text en © Howard Yanxon et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Yanxon, Howard
Weng, James
Parraga, Hannah
Xu, Wenqian
Ruett, Uta
Schwarz, Nicholas
Artifact identification in X-ray diffraction data using machine learning methods
title Artifact identification in X-ray diffraction data using machine learning methods
title_full Artifact identification in X-ray diffraction data using machine learning methods
title_fullStr Artifact identification in X-ray diffraction data using machine learning methods
title_full_unstemmed Artifact identification in X-ray diffraction data using machine learning methods
title_short Artifact identification in X-ray diffraction data using machine learning methods
title_sort artifact identification in x-ray diffraction data using machine learning methods
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814056/
https://www.ncbi.nlm.nih.gov/pubmed/36601933
http://dx.doi.org/10.1107/S1600577522011274
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