Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

Abstract Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fin...

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Autores principales:	Achintha Ihalage, Yang Hao
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/dd7d4d6b1a74428bad401fbb907fac8d
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spelling	oai:doaj.org-article:dd7d4d6b1a74428bad401fbb907fac8d2021-12-02T15:52:24ZAnalogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints10.1038/s41524-021-00536-22057-3960https://doaj.org/article/dd7d4d6b1a74428bad401fbb907fac8d2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00536-2https://doaj.org/toc/2057-3960Abstract Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in $$({{\rm{A}}}_{1-{\rm{x}}}{{\rm{A}}^{\prime} }_{{\rm{x}}}){{\rm{BO}}}_{3}$$ ( A 1 − x A ′ x ) BO 3 and $${\rm{A}}({{\rm{B}}}_{1-{\rm{x}}}{{\rm{B}}^{\prime} }_{{\rm{x}}}){{\rm{O}}}_{3}$$ A ( B 1 − x B ′ x ) O 3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.Achintha IhalageYang HaoNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (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 Achintha Ihalage Yang Hao Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
description	Abstract Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in $$({{\rm{A}}}_{1-{\rm{x}}}{{\rm{A}}^{\prime} }_{{\rm{x}}}){{\rm{BO}}}_{3}$$ ( A 1 − x A ′ x ) BO 3 and $${\rm{A}}({{\rm{B}}}_{1-{\rm{x}}}{{\rm{B}}^{\prime} }_{{\rm{x}}}){{\rm{O}}}_{3}$$ A ( B 1 − x B ′ x ) O 3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.
format	article
author	Achintha Ihalage Yang Hao
author_facet	Achintha Ihalage Yang Hao
author_sort	Achintha Ihalage
title	Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
title_short	Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
title_full	Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
title_fullStr	Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
title_full_unstemmed	Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
title_sort	analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dd7d4d6b1a74428bad401fbb907fac8d
work_keys_str_mv	AT achinthaihalage analogicaldiscoveryofdisorderedperovskiteoxidesbycrystalstructureinformationhiddeninunsupervisedmaterialfingerprints AT yanghao analogicaldiscoveryofdisorderedperovskiteoxidesbycrystalstructureinformationhiddeninunsupervisedmaterialfingerprints
_version_	1718385602485813248

Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

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