Predicting stable crystalline compounds using chemical similarity

Abstract We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantit...

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Autores principales:	Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques
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/f5b879cf046249a68b43d3dce63a79a8
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spelling	oai:doaj.org-article:f5b879cf046249a68b43d3dce63a79a82021-12-02T10:48:12ZPredicting stable crystalline compounds using chemical similarity10.1038/s41524-020-00481-62057-3960https://doaj.org/article/f5b879cf046249a68b43d3dce63a79a82021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00481-6https://doaj.org/toc/2057-3960Abstract We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to efficiently extract stable compounds from tremendously large initial sets, without any initial assumption on their crystal structures or chemical compositions.Hai-Chen WangSilvana BottiMiguel A. L. MarquesNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (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 Hai-Chen Wang Silvana Botti Miguel A. L. Marques Predicting stable crystalline compounds using chemical similarity
description	Abstract We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to efficiently extract stable compounds from tremendously large initial sets, without any initial assumption on their crystal structures or chemical compositions.
format	article
author	Hai-Chen Wang Silvana Botti Miguel A. L. Marques
author_facet	Hai-Chen Wang Silvana Botti Miguel A. L. Marques
author_sort	Hai-Chen Wang
title	Predicting stable crystalline compounds using chemical similarity
title_short	Predicting stable crystalline compounds using chemical similarity
title_full	Predicting stable crystalline compounds using chemical similarity
title_fullStr	Predicting stable crystalline compounds using chemical similarity
title_full_unstemmed	Predicting stable crystalline compounds using chemical similarity
title_sort	predicting stable crystalline compounds using chemical similarity
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f5b879cf046249a68b43d3dce63a79a8
work_keys_str_mv	AT haichenwang predictingstablecrystallinecompoundsusingchemicalsimilarity AT silvanabotti predictingstablecrystallinecompoundsusingchemicalsimilarity AT miguelalmarques predictingstablecrystallinecompoundsusingchemicalsimilarity
_version_	1718396685488488448

Predicting stable crystalline compounds using chemical similarity

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