Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

Abstract We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites a...

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Autores principales:	Pikee Priya, N. R. Aluru
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/df540142d82c42e48254d9c8bb7bed48
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spelling	oai:doaj.org-article:df540142d82c42e48254d9c8bb7bed482021-12-02T17:52:21ZAccelerated design and discovery of perovskites with high conductivity for energy applications through machine learning10.1038/s41524-021-00551-32057-3960https://doaj.org/article/df540142d82c42e48254d9c8bb7bed482021-06-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00551-3https://doaj.org/toc/2057-3960Abstract We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites and their classification based on the type of charge carrier at different conditions of temperature and environment. After evaluating a set of >100 features, we identify average ionic radius, minimum electronegativity, minimum atomic mass, minimum formation energy of oxides for all B-site, and B-site dopant ions of the perovskite as the crucial and relevant predictors for determining conductivity and the type of charge carriers. The models are validated by predicting the conductivity of compounds absent in the training set. We screen 1793 undoped and 95,832 A-site and B-site doped perovskites to report the perovskites with high conductivities, which can be used for different energy applications, depending on the type of the charge carriers.Pikee PriyaN. R. AluruNature 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 Pikee Priya N. R. Aluru Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
description	Abstract We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites and their classification based on the type of charge carrier at different conditions of temperature and environment. After evaluating a set of >100 features, we identify average ionic radius, minimum electronegativity, minimum atomic mass, minimum formation energy of oxides for all B-site, and B-site dopant ions of the perovskite as the crucial and relevant predictors for determining conductivity and the type of charge carriers. The models are validated by predicting the conductivity of compounds absent in the training set. We screen 1793 undoped and 95,832 A-site and B-site doped perovskites to report the perovskites with high conductivities, which can be used for different energy applications, depending on the type of the charge carriers.
format	article
author	Pikee Priya N. R. Aluru
author_facet	Pikee Priya N. R. Aluru
author_sort	Pikee Priya
title	Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
title_short	Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
title_full	Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
title_fullStr	Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
title_full_unstemmed	Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
title_sort	accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df540142d82c42e48254d9c8bb7bed48
work_keys_str_mv	AT pikeepriya accelerateddesignanddiscoveryofperovskiteswithhighconductivityforenergyapplicationsthroughmachinelearning AT nraluru accelerateddesignanddiscoveryofperovskiteswithhighconductivityforenergyapplicationsthroughmachinelearning
_version_	1718379257806192640

Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning

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