Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques

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.