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Machine Learning-Optimized Performance Enhancement of CH₃NH₃SnBr₃ Perovskite Solar Cells Using SCAPS-1D and wxAMPS

Introduction

Perovskite solar cells (PSCs) have revolutionized the field of photovoltaics due to their high efficiency, low-cost fabrication, and tunable properties. Among the various perovskite materials, CH₃NH₃SnBr₃ (Methylammonium Tin Bromide) is gaining attention as a lead-free alternative with promising optoelectronic properties. However, challenges like efficiency loss, stability issues, and charge recombination must be addressed for practical applications.

In this blog, we explore how machine learning (ML)-based optimization, combined with numerical simulations using SCAPS-1D and wxAMPS, can significantly enhance the performance of CH₃NH₃SnBr₃ PSCs by selecting the best charge transport materials (CTMs).

                                                                 


Why CH₃NH₃SnBr₃?

Lead-based perovskites like CH₃NH₃PbI₃ have dominated research due to their high power conversion efficiency (PCE). However, concerns over lead toxicity have prompted a search for eco-friendly alternatives. Tin-based perovskites such as CH₃NH₃SnBr₃ offer a non-toxic solution while maintaining good optoelectronic properties like:
✅ High absorption coefficient
✅ Direct bandgap (~1.8 eV, ideal for visible-light absorption)
✅ Potential for high efficiency in solar applications

Despite these advantages, tin perovskites suffer from high defect densities and rapid oxidation (Sn²⁺ → Sn⁴⁺), leading to performance degradation. Optimizing the charge transport layers (CTLs) can mitigate these issues.

The Role of Charge Transport Materials (CTMs)

Charge transport materials, including the electron transport layer (ETL) and hole transport layer (HTL), play a crucial role in determining device efficiency. The right choice of CTMs enhances charge extraction, reduces recombination, and improves stability.

Common CTMs used in perovskite solar cells:
🔹 Electron Transport Layers (ETLs): TiO₂, SnO₂, ZnO, PCBM
🔹 Hole Transport Layers (HTLs): Spiro-OMeTAD, Cu₂O, PEDOT:PSS, NiO

Selecting the best combination of ETL and HTL is key to improving the device’s overall performance.

Machine Learning for PSC Optimization

Machine learning has emerged as a powerful tool for material and device optimization in photovoltaics. By training ML models with experimental and simulation data, researchers can efficiently predict optimal CTMs, device structures, and operating conditions.

Key ML techniques used:
📌 Regression Models – Predict PCE based on material properties
📌 Neural Networks – Identify complex nonlinear relationships in device behavior
📌 Bayesian Optimization – Finds the best CTM combinations with minimal experiments

By integrating ML with numerical simulations (SCAPS-1D and wxAMPS), researchers can rapidly screen multiple CTM combinations and fine-tune parameters like:
🔹 Energy band alignment
🔹 Carrier mobility
🔹 Defect density reduction

SCAPS-1D & wxAMPS: Simulation Tools for Performance Enhancement

🔹 SCAPS-1D (Solar Cell Capacitance Simulator) is widely used for simulating PSCs by solving Poisson’s equation and continuity equations for charge carriers. It allows users to model defects, interface properties, and material characteristics.

🔹 wxAMPS (Advanced Semiconductor Device Simulator) provides an alternative approach to simulating multilayer solar cells, with a focus on recombination and transport mechanisms.

Using these tools, researchers can test different CTM configurations and validate ML predictions before moving to experimental fabrication.

Key Findings from ML-Based Optimization

🔸 Optimized CTM Combinations: ML-assisted simulations suggest that SnO₂ (ETL) and Cu₂O (HTL) offer the best performance for CH₃NH₃SnBr₃-based PSCs, minimizing charge recombination.

🔸 Band Alignment Improvement: Adjusting energy band offsets between CH₃NH₃SnBr₃ and CTMs enhances charge transport efficiency.

🔸 Efficiency Boost: ML-guided optimization predicts an efficiency increase of ~20-30% compared to conventional trial-and-error approaches.

Conclusion & Future Outlook

Machine learning is revolutionizing PSC research by accelerating material discovery and device optimization. By leveraging SCAPS-1D and wxAMPS, we can efficiently design high-performance CH₃NH₃SnBr₃ perovskite solar cells with optimal charge transport materials.

Future directions include:
🔹 Further enhancing stability with doped CTMs
🔹 Expanding ML models with real-world experimental datasets
🔹 Exploring new, cost-effective, and scalable fabrication methods

As ML-driven solar cell optimization advances, tin-based perovskites could soon become a viable alternative to lead-based counterparts, paving the way for eco-friendly, high-efficiency photovoltaics.

30th Edition of International Research Conference on Science Health and Engineering | 28-29 March 2025 | San Francisco, United States

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