Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques
Vijay C. Karade,
Santosh S. Sutar,
Jun Sung Jang,
Kuldeep Singh Gour,
Seung Wook Shin,
Mahesh P. Suryawanshi,
Rajanish K. Kamat,
Tukaram D. Dongale,
Jin Hyeok Kim,
Jae Ho Yun
Affiliations
Vijay C. Karade
Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Naju 58330, Republic of Korea
Santosh S. Sutar
Yashwantrao Chavan School of Rural Development, Shivaji University, Kolhapur 416004, India
Jun Sung Jang
Optoelectronics Convergence Research Center, Department of Materials Science and Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
Future Agricultural Research Division, Rural Research Institute, Korea Rural Community Corporation, Ansan-si 15634, Republic of Korea
Mahesh P. Suryawanshi
School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Rajanish K. Kamat
Department of Electronics, Shivaji University, Kolhapur 416004, India
Tukaram D. Dongale
Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur 416004, India
Jin Hyeok Kim
Optoelectronics Convergence Research Center, Department of Materials Science and Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
Jae Ho Yun
Department of Energy Engineering, Korea Institute of Energy Technology (KENTECH), Naju 58330, Republic of Korea
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.
