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Updated: Feb 17, 2026
Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance
Published on: February 27, 2017
Performance optimization and machine learning-guided parameter sensitivity analysis of lead-free KGeCl3 perovskite
Tanzir Ahamed1,2, Md Mehedi Hasan Bappy1,2, Mohammad Rahimul Islam1
1Department of Electrical and Electronic Engineering, CCN University of Science and Technology Cumilla-3503 Bangladesh tanzir.eee2k15@gmail.com.
None:
This study gives a realistic insight into the effectiveness of lead-free Ge-based perovskite solar cells (PSCs) using KGeCl3 as the absorber layer in combination with four different electron transport layers (ETLs), including WS2, ZnSe, PC60BM, and SnS2, with copper iron tin sulfide (CFTS) serving as the hole transport layer (HTL). Initially, key material parameters such as layer thickness, donor density (N D), acceptor density (N A), defect density (N t), interface defect densities (IL1 & IL2), series resistance (R s), shunt resistance (R sh), operating temperature (K), and back contact work function (eV) are varied using a SCAPS-1D simulator to optimize device performance. Between the four cell configurations, the FTO/CFTS/KGeCl3/WS2/Au structure has achieved the highest performance with a power conversion efficiency (PCE) of 21.39%, short-circuit current density (J SC) of 39.526 mA cm-2, fill factor (FF) of 76.56%, and open-circuit voltage (V OC) of 0.706 V at a simulated temperature of 300 K. Other configurations using ZnSe, PC60BM, and SnS2 as ETLs showed PCE values of 21.38%, 21.05%, and 20.43%, respectively. Furthermore, an integrated machine learning framework with four supervised learning methods, i.e., Random Forest, XGBoost, CatBoost, and Decision Tree, has been utilized to effectively evaluate the importance of material features. Out of the algorithms, CatBoost has the highest performance with R 2 and accuracy values of 0.984 and 99.344%, respectively.
