Two-dimensional Janus material MoS2(1-x)Se2x (0 < x < 1) for photovoltaic applications: A machine learning and density functional study
[Display omitted] •Random Forest method achieves a high precision in predicting the PCE of MoS2(1-x)Se2x (0<x<1).•A high PCE larger than 18% is found in MoS2(1-x)Se2x with Se concentrations varied from 32% to 96%.•MoS0.89Se1.11 has potential application in solar cell due to high optical absorp...
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Published in | Computational materials science Vol. 186; p. 109998 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.01.2021
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Abstract | [Display omitted]
•Random Forest method achieves a high precision in predicting the PCE of MoS2(1-x)Se2x (0<x<1).•A high PCE larger than 18% is found in MoS2(1-x)Se2x with Se concentrations varied from 32% to 96%.•MoS0.89Se1.11 has potential application in solar cell due to high optical absorption intensity.
Janus transition-metal dichalcogenides MoSSe has been attracted much attention due to its excellent electronic properties induced by mirror symmetry breaking. In this work, based on machine learning and density functional theory, the photoelectric conversion coefficient (PCE) along with the variation of Se concentration in MoS2(1-x)Se2x (0 < x < 1) are explored. Ten most important features are sorted out by calculating the Pearson correlation coefficient matrices to identify the linear relationship between any two features and their correlation. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute relative error (MARE) are evaluated for the built machine learning models of random forest (RF) and multiple linear regression (MLR). For the prediction of the PCE, the RF algorithm using structural information and computational features obtained from DFT calculations is validated possessing high efficiency. In a wide range of doping concentrations (n = 32%–96%), we predict that the PCE of MoS2(1-x)Se2x has a high value larger than 18%. High optical absorption intensity in an order of 105 cm−1 is obtained in MoS0.89Se1.11, which has potential application in solar cell. |
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AbstractList | [Display omitted]
•Random Forest method achieves a high precision in predicting the PCE of MoS2(1-x)Se2x (0<x<1).•A high PCE larger than 18% is found in MoS2(1-x)Se2x with Se concentrations varied from 32% to 96%.•MoS0.89Se1.11 has potential application in solar cell due to high optical absorption intensity.
Janus transition-metal dichalcogenides MoSSe has been attracted much attention due to its excellent electronic properties induced by mirror symmetry breaking. In this work, based on machine learning and density functional theory, the photoelectric conversion coefficient (PCE) along with the variation of Se concentration in MoS2(1-x)Se2x (0 < x < 1) are explored. Ten most important features are sorted out by calculating the Pearson correlation coefficient matrices to identify the linear relationship between any two features and their correlation. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute relative error (MARE) are evaluated for the built machine learning models of random forest (RF) and multiple linear regression (MLR). For the prediction of the PCE, the RF algorithm using structural information and computational features obtained from DFT calculations is validated possessing high efficiency. In a wide range of doping concentrations (n = 32%–96%), we predict that the PCE of MoS2(1-x)Se2x has a high value larger than 18%. High optical absorption intensity in an order of 105 cm−1 is obtained in MoS0.89Se1.11, which has potential application in solar cell. |
ArticleNumber | 109998 |
Author | Zhang, Guanhua Yuan, Jianmei Mao, Yuliang Huang, Yunqing |
Author_xml | – sequence: 1 givenname: Guanhua surname: Zhang fullname: Zhang, Guanhua organization: Hunan Key Laboratory for Computation and Simulation in Science and Engineering, School of Mathematics and Computational Science, Xiangtan University, China – sequence: 2 givenname: Jianmei surname: Yuan fullname: Yuan, Jianmei email: yuanjm@xtu.edu.cn organization: Hunan Key Laboratory for Computation and Simulation in Science and Engineering, School of Mathematics and Computational Science, Xiangtan University, China – sequence: 3 givenname: Yuliang orcidid: 0000-0003-1391-914X surname: Mao fullname: Mao, Yuliang email: ylmao@xtu.edu.cn organization: Hunan Key Laboratory for Micro-Nano Energy Materials and Devices, School of Physics and Optoelectronic, Xiangtan University, Hunan 411105, China – sequence: 4 givenname: Yunqing surname: Huang fullname: Huang, Yunqing organization: Hunan Key Laboratory for Computation and Simulation in Science and Engineering, School of Mathematics and Computational Science, Xiangtan University, China |
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Keywords | Janus transition-metal dichalcogenides Density functional theory Photoelectric conversion coefficient Machine learning |
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•Random Forest method achieves a high precision in predicting the PCE of MoS2(1-x)Se2x (0<x<1).•A high PCE larger than 18% is found in... |
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SubjectTerms | Density functional theory Janus transition-metal dichalcogenides Machine learning Photoelectric conversion coefficient |
Title | Two-dimensional Janus material MoS2(1-x)Se2x (0 < x < 1) for photovoltaic applications: A machine learning and density functional study |
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