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First-Principles DFT Investigation of CsSn0.5Ge0.5I3 and Machine Learning-Assisted Numerical Simulation of Lead-Free
Qinmiao Yu1, Jinglan Liang1, Xueji Chang1
1College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Materials (Basel, Switzerland)
|August 13, 2026
Summary
This study explores lead-free perovskite solar cells (PSCs) using advanced simulations. Optimized structures achieve high power conversion efficiency (PCE), offering a sustainable path for next-generation solar technology.
Area of Science:
- Materials Science
- Solid-State Physics
- Renewable Energy
Background:
- Perovskite solar cells (PSCs) offer promising photovoltaic performance.
- Lead-free perovskites are sought after for environmental sustainability.
- CsSn0.5Ge0.5I3 is a potential candidate for lead-free PSCs.
Purpose of the Study:
- Investigate the optoelectronic properties of lead-free CsSn0.5Ge0.5I3.
- Optimize device structure and parameters for high-performance PSCs.
- Explore machine learning for predicting photovoltaic parameters.
Main Methods:
- First-principles calculations and SCAPS-1D simulations.
- Evaluation of energy-level alignment for transport layers.
- Optimization of bulk defect density, layer thickness, and electrode materials.
Main Results:
- Optimal device structure (FTO/PCBM/CsSn0.5Ge0.5I3/PEDOT:PSS/C) achieved 24.50% PCE and 80.01% FF.
- Analysis of resistance, illumination, thermal stability, and carrier dynamics.
- Random Forest (RF) machine learning model showed highest accuracy in parameter prediction.
Conclusions:
- Absorber layer thickness identified as key factor for efficiency via SHAP analysis.
- Integrated simulation and ML approach provides pathway for designing stable, high-performance PSCs.
- Demonstrates potential for environmentally sustainable perovskite solar cell development.

