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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

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This study explores lead-free Germanium-based perovskite solar cells (PSCs) with KGeCl3 absorbers. The WS2 electron transport layer (ETL) configuration achieved the highest power conversion efficiency (PCE) of 21.39%.

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Area of Science:

  • Materials Science
  • Renewable Energy
  • Photovoltaics

Background:

  • Perovskite solar cells (PSCs) offer promising photovoltaic performance.
  • Developing lead-free PSCs is crucial for environmental sustainability.
  • Germanium-based perovskites are emerging as a viable alternative to lead-based materials.

Purpose of the Study:

  • To investigate the performance of lead-free Ge-based PSCs.
  • To evaluate different electron transport layers (ETLs) for KGeCl3 absorber-based devices.
  • To optimize PSC parameters using simulation and machine learning.

Main Methods:

  • Device simulation using SCAPS-1D to optimize material parameters (thickness, doping, defects, resistance, temperature, work function).
  • Fabrication and characterization of PSCs with KGeCl3 absorber and various ETLs (WS2, ZnSe, PC60BM, SnS2).
  • Application of machine learning (Random Forest, XGBoost, CatBoost, Decision Tree) for feature importance analysis.

Main Results:

  • The FTO/CFTS/KGeCl3/WS2/Au solar cell configuration demonstrated the highest power conversion efficiency (PCE) of 21.39%.
  • Other ETLs yielded PCEs of 21.38% (ZnSe), 21.05% (PC60BM), and 20.43% (SnS2).
  • CatBoost machine learning model achieved high accuracy (99.344%) and R² (0.984) in evaluating material feature importance.

Conclusions:

  • Lead-free Ge-based PSCs with KGeCl3 absorbers are highly effective.
  • WS2 is an optimal ETL for enhancing the performance of these solar cells.
  • Machine learning provides valuable insights into material parameter optimization for PSCs.