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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

In the Kesterite family, the Cu2ZnSn(S,Se)4 (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, optical, and electrical properties of the CZTSSe absorber layer. So, in this work, a machine learning (ML) approach is employed to evaluate effect composition ratio on the device parameters of CZTSSe TFSCs. In particular, the bi-metallic ratios like Cu/Sn, Zn/Sn, Cu/Zn, and overall Cu/(Zn+Sn) cation composition ratio are investigated. To achieve this, different machine learning algorithms, such as decision trees (DTs) and classification and regression trees (CARTs), are used. In addition, the output performance parameters of CZTSSe TFSCs are predicted by both continuous and categorical approaches. Artificial neural networks (ANN) and XGBoost (XGB) algorithms are employed for the continuous approach. On the other hand, support vector machine and k-nearest neighbor’s algorithms are also used for the categorical approach. Through the analysis, it is observed that the DT and CART algorithms provided a critical composition range well suited for the fabrication of highly efficient CZTSSe TFSCs, while the XGB and ANN showed better prediction accuracy among the tested algorithms. The present work offers valuable guidance towards the integration of the ML approach with experimental studies in the field of TFSCs.

Details

Title
Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques
Author
Karade, Vijay C 1   VIAFID ORCID Logo  ; Sutar, Santosh S 2 ; Jun Sung Jang 3 ; Kuldeep Singh Gour 4 ; Shin, Seung Wook 5 ; Suryawanshi, Mahesh P 6 ; Kamat, Rajanish K 7 ; Dongale, Tukaram D 8   VIAFID ORCID Logo  ; Kim, Jin Hyeok 3 ; Jae Ho Yun 9 

 Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Naju 58330, Republic of Korea; [email protected]; Optoelectronics Convergence Research Center, Department of Materials Science and Engineering, Chonnam National University, Gwangju 61186, Republic of Korea; [email protected] 
 Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur 416004, India; [email protected] 
 Optoelectronics Convergence Research Center, Department of Materials Science and Engineering, Chonnam National University, Gwangju 61186, Republic of Korea; [email protected] 
 Surface Engineering Group, Advanced Materials & Processes Division, CSIR-National Metallurgical Laboratory, Jamshedpur 831007, India; [email protected] 
 Future Agricultural Research Division, Rural Research Institute, Korea Rural Community Corporation, Ansan-si 15634, Republic of Korea; [email protected] 
 School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia; [email protected] 
 Department of Electronics, Shivaji University, Kolhapur 416004, India; [email protected]; The Institute of Science,. Homi Bhabha State University, 15 Madam Cama Road, Mumbai 400032, India 
 Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India 
 Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Naju 58330, Republic of Korea; [email protected] 
First page
1581
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20734352
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2893029339
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.