Bandgap prediction of two-dimensional materials using machine learning.

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for elec...

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Autores principales:	Yu Zhang, Wenjing Xu, Guangjie Liu, Zhiyong Zhang, Jinlong Zhu, Meng Li
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/448941464d5948468b9f2f67607b6209
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id	oai:doaj.org-article:448941464d5948468b9f2f67607b6209
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spelling	oai:doaj.org-article:448941464d5948468b9f2f67607b62092021-12-02T20:15:02ZBandgap prediction of two-dimensional materials using machine learning.1932-620310.1371/journal.pone.0255637https://doaj.org/article/448941464d5948468b9f2f67607b62092021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255637https://doaj.org/toc/1932-6203The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.Yu ZhangWenjing XuGuangjie LiuZhiyong ZhangJinlong ZhuMeng LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255637 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Zhang Wenjing Xu Guangjie Liu Zhiyong Zhang Jinlong Zhu Meng Li Bandgap prediction of two-dimensional materials using machine learning.
description	The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.
format	article
author	Yu Zhang Wenjing Xu Guangjie Liu Zhiyong Zhang Jinlong Zhu Meng Li
author_facet	Yu Zhang Wenjing Xu Guangjie Liu Zhiyong Zhang Jinlong Zhu Meng Li
author_sort	Yu Zhang
title	Bandgap prediction of two-dimensional materials using machine learning.
title_short	Bandgap prediction of two-dimensional materials using machine learning.
title_full	Bandgap prediction of two-dimensional materials using machine learning.
title_fullStr	Bandgap prediction of two-dimensional materials using machine learning.
title_full_unstemmed	Bandgap prediction of two-dimensional materials using machine learning.
title_sort	bandgap prediction of two-dimensional materials using machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/448941464d5948468b9f2f67607b6209
work_keys_str_mv	AT yuzhang bandgappredictionoftwodimensionalmaterialsusingmachinelearning AT wenjingxu bandgappredictionoftwodimensionalmaterialsusingmachinelearning AT guangjieliu bandgappredictionoftwodimensionalmaterialsusingmachinelearning AT zhiyongzhang bandgappredictionoftwodimensionalmaterialsusingmachinelearning AT jinlongzhu bandgappredictionoftwodimensionalmaterialsusingmachinelearning AT mengli bandgappredictionoftwodimensionalmaterialsusingmachinelearning
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Bandgap prediction of two-dimensional materials using machine learning.

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