Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological c...

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Autores principales:	Scott Broderick, Ruhil Dongol, Tianmu Zhang, Krishna Rajan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9134fcce99cf4ca2ad1ff3ff24fd1ef6
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spelling	oai:doaj.org-article:9134fcce99cf4ca2ad1ff3ff24fd1ef62021-12-02T15:57:04ZClassification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps10.1038/s41598-021-90070-42045-2322https://doaj.org/article/9134fcce99cf4ca2ad1ff3ff24fd1ef62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90070-4https://doaj.org/toc/2045-2322Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.Scott BroderickRuhil DongolTianmu ZhangKrishna RajanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Scott Broderick Ruhil Dongol Tianmu Zhang Krishna Rajan Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
description	Abstract This paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.
format	article
author	Scott Broderick Ruhil Dongol Tianmu Zhang Krishna Rajan
author_facet	Scott Broderick Ruhil Dongol Tianmu Zhang Krishna Rajan
author_sort	Scott Broderick
title	Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
title_short	Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
title_full	Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
title_fullStr	Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
title_full_unstemmed	Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps
title_sort	classification of apatite structures via topological data analysis: a framework for a ‘materials barcode’ representation of structure maps
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9134fcce99cf4ca2ad1ff3ff24fd1ef6
work_keys_str_mv	AT scottbroderick classificationofapatitestructuresviatopologicaldataanalysisaframeworkforamaterialsbarcoderepresentationofstructuremaps AT ruhildongol classificationofapatitestructuresviatopologicaldataanalysisaframeworkforamaterialsbarcoderepresentationofstructuremaps AT tianmuzhang classificationofapatitestructuresviatopologicaldataanalysisaframeworkforamaterialsbarcoderepresentationofstructuremaps AT krishnarajan classificationofapatitestructuresviatopologicaldataanalysisaframeworkforamaterialsbarcoderepresentationofstructuremaps
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Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

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