Performance optimization and machine learning-guided parameter sensitivity analysis of lead-free KGeCl3 perovskite solar cells
Tanzir Ahamed, Md. Mehedi Hasan Bappy, Mohammad Rahimul Islam, Md. Shihab Uddin, Md. Arafat Hossain, Tanvir Ahammed
TL;DR
This study explores lead-free perovskite solar cells using KGeCl3 and different electron transport layers, optimizing performance with simulation and machine learning.
Contribution
The novel use of machine learning to guide parameter sensitivity analysis in lead-free perovskite solar cells is introduced.
Findings
The FTO/CFTS/KGeCl3/WS2/Au configuration achieved the highest power conversion efficiency of 21.39%.
CatBoost machine learning algorithm outperformed others with an R2 score of 0.984 and 99.344% accuracy.
WS2 as an electron transport layer showed the best performance in terms of band alignment and recombination.
Abstract
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 (ND), acceptor density (NA), defect density (Nt), interface defect densities (IL1 & IL2), series resistance (Rs), shunt resistance (Rsh), 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 (JSC) of 39.526 mA cm−2,…
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Taxonomy
TopicsPerovskite Materials and Applications · Machine Learning in Materials Science · Heusler alloys: electronic and magnetic properties