Unsupervised discovery of thin-film photovoltaic materials from unlabeled data

Abstract Quaternary chalcogenide semiconductors (I2-II-IV-X4) are key materials for thin-film photovoltaics (PVs) to alleviate the energy crisis. Scaling up of PVs requires the discovery of I2-II-IV-X4 with good photoelectric properties; however, the structure search space is significantly large to...

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Autores principales:	Zhilong Wang, Junfei Cai, Qingxun Wang, SiCheng Wu, Jinjin Li
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/2e09b890c52a43408b19d9a518475265
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spelling	oai:doaj.org-article:2e09b890c52a43408b19d9a5184752652021-12-02T18:51:00ZUnsupervised discovery of thin-film photovoltaic materials from unlabeled data10.1038/s41524-021-00596-42057-3960https://doaj.org/article/2e09b890c52a43408b19d9a5184752652021-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00596-4https://doaj.org/toc/2057-3960Abstract Quaternary chalcogenide semiconductors (I2-II-IV-X4) are key materials for thin-film photovoltaics (PVs) to alleviate the energy crisis. Scaling up of PVs requires the discovery of I2-II-IV-X4 with good photoelectric properties; however, the structure search space is significantly large to explore exhaustively. The scarcity of available data impedes even many machine learning (ML) methods. Here, we employ the unsupervised learning (UL) method to discover I2-II-IV-X4 that alleviates the challenge of data scarcity. We screen all the I2-II-IV-X4 from the periodic table as the initial data and finally select eight candidates through UL. As predicted by ab initio calculations, they exhibit good optical conversion efficiency, strong optical responses, and good thermal stabilities at room temperatures. This typical case demonstrates the potential of UL in material discovery, which overcomes the limitation of data scarcity, and shortens the computational screening cycle of I2-II-IV-X4 by ~12.1 years, providing a research avenue for rapid material discovery.Zhilong WangJunfei CaiQingxun WangSiCheng WuJinjin LiNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-11 (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 Zhilong Wang Junfei Cai Qingxun Wang SiCheng Wu Jinjin Li Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
description	Abstract Quaternary chalcogenide semiconductors (I2-II-IV-X4) are key materials for thin-film photovoltaics (PVs) to alleviate the energy crisis. Scaling up of PVs requires the discovery of I2-II-IV-X4 with good photoelectric properties; however, the structure search space is significantly large to explore exhaustively. The scarcity of available data impedes even many machine learning (ML) methods. Here, we employ the unsupervised learning (UL) method to discover I2-II-IV-X4 that alleviates the challenge of data scarcity. We screen all the I2-II-IV-X4 from the periodic table as the initial data and finally select eight candidates through UL. As predicted by ab initio calculations, they exhibit good optical conversion efficiency, strong optical responses, and good thermal stabilities at room temperatures. This typical case demonstrates the potential of UL in material discovery, which overcomes the limitation of data scarcity, and shortens the computational screening cycle of I2-II-IV-X4 by ~12.1 years, providing a research avenue for rapid material discovery.
format	article
author	Zhilong Wang Junfei Cai Qingxun Wang SiCheng Wu Jinjin Li
author_facet	Zhilong Wang Junfei Cai Qingxun Wang SiCheng Wu Jinjin Li
author_sort	Zhilong Wang
title	Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
title_short	Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
title_full	Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
title_fullStr	Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
title_full_unstemmed	Unsupervised discovery of thin-film photovoltaic materials from unlabeled data
title_sort	unsupervised discovery of thin-film photovoltaic materials from unlabeled data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2e09b890c52a43408b19d9a518475265
work_keys_str_mv	AT zhilongwang unsuperviseddiscoveryofthinfilmphotovoltaicmaterialsfromunlabeleddata AT junfeicai unsuperviseddiscoveryofthinfilmphotovoltaicmaterialsfromunlabeleddata AT qingxunwang unsuperviseddiscoveryofthinfilmphotovoltaicmaterialsfromunlabeleddata AT sichengwu unsuperviseddiscoveryofthinfilmphotovoltaicmaterialsfromunlabeleddata AT jinjinli unsuperviseddiscoveryofthinfilmphotovoltaicmaterialsfromunlabeleddata
_version_	1718377436946628608

Unsupervised discovery of thin-film photovoltaic materials from unlabeled data

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