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

Abstract 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.