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 inComputational materials science Vol. 186; p. 109998
Main Authors Zhang, Guanhua, Yuan, Jianmei, Mao, Yuliang, Huang, Yunqing
Format Journal Article
LanguageEnglish
Published 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.
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
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Keywords Janus transition-metal dichalcogenides
Density functional theory
Photoelectric conversion coefficient
Machine learning
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  ident: 10.1016/j.commatsci.2020.109998_b0165
  contributor:
    fullname: Lison
– volume: 26
  start-page: 2648
  year: 2014
  ident: 10.1016/j.commatsci.2020.109998_b0060
  article-title: Growth of large-area 2D MoS2(1–x)Se2x semiconductor alloys
  publication-title: Adv. Mater.
  doi: 10.1002/adma.201306095
  contributor:
    fullname: Feng
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Snippet [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...
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StartPage 109998
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
URI https://dx.doi.org/10.1016/j.commatsci.2020.109998
Volume 186
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