Machine learning for perovskite materials design and discovery

Abstract The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we...

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Autores principales:	Qiuling Tao, Pengcheng Xu, Minjie Li, Wencong Lu
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/bf319000fe4f4a69bdf90cccef7de71a
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spelling	oai:doaj.org-article:bf319000fe4f4a69bdf90cccef7de71a2021-12-02T13:24:35ZMachine learning for perovskite materials design and discovery10.1038/s41524-021-00495-82057-3960https://doaj.org/article/bf319000fe4f4a69bdf90cccef7de71a2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00495-8https://doaj.org/toc/2057-3960Abstract The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.Qiuling TaoPengcheng XuMinjie LiWencong LuNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-18 (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 Qiuling Tao Pengcheng Xu Minjie Li Wencong Lu Machine learning for perovskite materials design and discovery
description	Abstract The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.
format	article
author	Qiuling Tao Pengcheng Xu Minjie Li Wencong Lu
author_facet	Qiuling Tao Pengcheng Xu Minjie Li Wencong Lu
author_sort	Qiuling Tao
title	Machine learning for perovskite materials design and discovery
title_short	Machine learning for perovskite materials design and discovery
title_full	Machine learning for perovskite materials design and discovery
title_fullStr	Machine learning for perovskite materials design and discovery
title_full_unstemmed	Machine learning for perovskite materials design and discovery
title_sort	machine learning for perovskite materials design and discovery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bf319000fe4f4a69bdf90cccef7de71a
work_keys_str_mv	AT qiulingtao machinelearningforperovskitematerialsdesignanddiscovery AT pengchengxu machinelearningforperovskitematerialsdesignanddiscovery AT minjieli machinelearningforperovskitematerialsdesignanddiscovery AT wenconglu machinelearningforperovskitematerialsdesignanddiscovery
_version_	1718393056169820160

Machine learning for perovskite materials design and discovery

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