Symmetry-aware recursive image similarity exploration for materials microscopy

Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific di...

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Autores principales:	Tri N. M. Nguyen, Yichen Guo, Shuyu Qin, Kylie S. Frew, Ruijuan Xu, Joshua C. Agar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/928ea7d232ed406c8c9ab3b327b39c77
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spelling	oai:doaj.org-article:928ea7d232ed406c8c9ab3b327b39c772021-12-02T19:16:18ZSymmetry-aware recursive image similarity exploration for materials microscopy10.1038/s41524-021-00637-y2057-3960https://doaj.org/article/928ea7d232ed406c8c9ab3b327b39c772021-10-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00637-yhttps://doaj.org/toc/2057-3960Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package ( https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer ) of this interactive tool for researchers to use with their data.Tri N. M. NguyenYichen GuoShuyu QinKylie S. FrewRuijuan XuJoshua C. AgarNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-14 (2021)
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 Tri N. M. Nguyen Yichen Guo Shuyu Qin Kylie S. Frew Ruijuan Xu Joshua C. Agar Symmetry-aware recursive image similarity exploration for materials microscopy
description	Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package ( https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer ) of this interactive tool for researchers to use with their data.
format	article
author	Tri N. M. Nguyen Yichen Guo Shuyu Qin Kylie S. Frew Ruijuan Xu Joshua C. Agar
author_facet	Tri N. M. Nguyen Yichen Guo Shuyu Qin Kylie S. Frew Ruijuan Xu Joshua C. Agar
author_sort	Tri N. M. Nguyen
title	Symmetry-aware recursive image similarity exploration for materials microscopy
title_short	Symmetry-aware recursive image similarity exploration for materials microscopy
title_full	Symmetry-aware recursive image similarity exploration for materials microscopy
title_fullStr	Symmetry-aware recursive image similarity exploration for materials microscopy
title_full_unstemmed	Symmetry-aware recursive image similarity exploration for materials microscopy
title_sort	symmetry-aware recursive image similarity exploration for materials microscopy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/928ea7d232ed406c8c9ab3b327b39c77
work_keys_str_mv	AT trinmnguyen symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy AT yichenguo symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy AT shuyuqin symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy AT kyliesfrew symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy AT ruijuanxu symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy AT joshuacagar symmetryawarerecursiveimagesimilarityexplorationformaterialsmicroscopy
_version_	1718376985765347328

Symmetry-aware recursive image similarity exploration for materials microscopy

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