Key factors governing the device performance of CIGS solar cells: Insights from machine learning

Zhu, Chengwan; Liu, Wu; Li, Yaoyao; Huo, Xiaomin; Li, Haotian; Guo, Kai; Qiao, Bo; Zhao, Suling; Xu, Zheng; Zhao, Hongyu; Song, Dandan

doi:10.1016/j.solener.2021.09.031

Cited by 15 publications

(12 citation statements)

References 41 publications

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“…[ 26–28 ] Lately, these ML techniques have been used for chalcogenide‐based TFSCs such as CIGS and CZTSSe. [ 22,23,29 ] Zhu et al. [ 22 ] established the correlations between the device performances of CIGS TFSCs and different preparative parameters through an artificial neural network (ANN), random forest (RF), and linear regression ML algorithms.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Karade

Sutar

Shin

et al. 2023

Adv Funct Materials

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The Earth‐abundant element‐based Cu2ZnSn(S,Se)4 (CZTSSe) absorber is considered as a promising material for thin‐film solar cells (TFSCs). The current record power conversion efficiency (PCE) of CZTSSe TFSCs is ≈13%, and it's still lower than CdTe and CIGS‐based TFSCs. A further breakthrough in its PCE mainly relies on deep insights into the various device fabrication conditions; accordingly, the experimental–oriented machine learning (ML) approach can be an effective way to discover key governing factors in improving PCE. The present work aims to identify the key governing factors throughout the device fabrication processes and apply them to break the saturated PCE for CZTSSe TFSCs. For realization, over 25,000 data points were broadly collected by fabricating more than 1300 CZTSSe TFSC devices and analyzed them using various ML techniques. Through extensive ML analysis, the i‐ZnO thickness is found to be the first, while Zn/Sn compositional ratio and sulfo‐selenization temperature are other key governing factors under thin or thick i‐ZnO thickness to achieve over 11% PCE. Based on these key governing factors, the applied random forest ML prediction model for PCE showed Adj. R2 = >0.96. Finally, the best‐predicted ML conditions considered for experimental validation showed well‐matched experimental outcomes with different ML models.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Karade

Sutar

Shin

et al. 2023

Adv Funct Materials

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“…It has been used to understand perovskite properties based on the structures/ compositions and to develop new perovskites, 22,23 select the capping layer for perovskite lms, 24 explore suitable perovskites for use in highly efficient PSCs, 25 screen candidates for use in organic solar cells, 26 and identify the key factors governing the device performances of Cu(In,Ga)Se 2 solar cells. 27 These previous attempts reveal the power of machine learning in processing the complex relationships between materials and material/device performance. Therefore, it is also possible that machine learning could be used to help intelligently screen organic additives or interface materials for use in highly efficient PSCs, mapping correlations, but such an attempt is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Liu

Wei

et al. 2022

J. Mater. Chem. A

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“…[19] There are also ML works published by other investigators involving PSC [20][21][22] as well as other solar cell technologies. [23][24][25][26][27][28] However, as far as we know, there is no comprehensive work published to assess the efficiency and stability of 2D/3D perovskites using ML tools.…”

Section: Introductionmentioning

confidence: 99%

Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning

2022

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A dataset containing 599 data points from 146 publications on 2D/3D perovskite solar cells is analyzed using machine learning. The predictive models are developed for power conversion efficiency (PCE) using eXtreme Gradient Boosting regression, random forest regression and artificial neural networks while association rule mining is used to analyze the stability data to identify the descriptors leading to high stability 2D/3D cells. A predictive model is also developed for the bandgap to predict the missing values in the dataset for the use in PCE predictions. Models for both bandgap and PCE predictions are quite successful. The thickness of inorganic layer (n), radius of anion (R x ), and 2D cation (R m) are found to be the most important descriptors for bandgap predictions; n and R m, together with the bandgap, are found to be deterministic for PCE in regular cells while the bandgap, n, and conduction band energy of hole transport layer are the most influential descriptors in inverted structures. Association rule mining analysis for the stability indicates that the cells with layered perovskite structures are more stable while the 2D and 3D cations leading to the most stable cells are found to be butylammonium and formamidinium‐Cs mixed cation respectively.

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Key factors governing the device performance of CIGS solar cells: Insights from machine learning

Cited by 15 publications

References 41 publications

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells

Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning

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