Optimizing Perovskite Solar Cell Stability via High-Throughput Catalyst Screening

Accelerating the Discovery of Durable Perovskite Materials

Perovskite solar cells (PSCs) have emerged as a revolutionary photovoltaic technology, offering high efficiency, low production costs, and tunable optoelectronic properties. However, their commercial viability is hindered by stability issues under environmental stressors such as moisture, heat, and UV radiation. High-throughput catalyst screening combined with machine learning presents a transformative approach to rapidly identify durable perovskite compositions.

The Stability Challenge in Perovskite Solar Cells

While PSCs have achieved certified power conversion efficiencies exceeding 25%, their operational lifetimes remain inferior to silicon-based counterparts. Key degradation mechanisms include:

• Phase segregation under illumination and electric fields
• Ion migration leading to hysteresis and performance decay
• Chemical decomposition at electrode interfaces
• Moisture-induced degradation of the perovskite lattice

High-Throughput Screening: A Paradigm Shift

Traditional trial-and-error approaches for stability optimization are time-consuming and resource-intensive. High-throughput experimentation enables:

• Parallel synthesis of hundreds of material variants
• Automated characterization of stability metrics
• Rapid identification of degradation-resistant compositions
• Systematic exploration of dopants and interface modifiers

Key Components of High-Throughput Workflows

Modern screening platforms integrate multiple advanced techniques:

• Automated inkjet printing for precise composition control
• Robotic spin-coating systems with environmental control
• In-situ X-ray diffraction for phase stability monitoring
• Multi-channel aging chambers with varied stress conditions
• Automated current-voltage characterization

Machine Learning for Accelerated Discovery

The vast datasets generated from high-throughput experiments require sophisticated analysis tools. Machine learning approaches provide:

• Pattern recognition in complex degradation pathways
• Predictive models for long-term stability
• Optimization algorithms for composition space exploration
• Feature importance analysis for mechanistic insights

Successful Applications of ML in Stability Prediction

Recent studies demonstrate machine learning's capability to:

• Predict moisture resistance from elemental descriptors
• Identify optimal A-site cation mixtures for phase stability
• Design effective passivation molecules from structural fingerprints
• Optimize interface layers for reduced ion migration

Catalyst Screening for Interface Engineering

Interface degradation accounts for >60% of PSC failure modes. High-throughput catalyst screening enables:

• Rapid evaluation of charge extraction layers
• Discovery of novel hole transport materials
• Optimization of electron-selective contacts
• Screening of molecular passivators

Case Study: Metal Oxide Catalysts for Stability Enhancement

A recent high-throughput study screened 127 metal oxide compositions as electron transport layers. The automated workflow revealed:

• SnO₂-ZnO composites with superior UV stability
• Titanium-doped ZnO showing reduced interfacial recombination
• Nb₂O₅ as an effective moisture barrier layer

Material Informatics for Composition Optimization

The multidimensional nature of perovskite stability requires advanced data science approaches:

• Descriptor engineering: Developing quantitative structure-stability relationships
• Active learning: Iterative experimental design based on model predictions
• Transfer learning: Applying knowledge from related material systems
• Bayesian optimization: Efficient navigation of vast composition spaces

Tandem Experimental-Computational Workflows

The most effective strategies combine:

1. High-throughput synthesis of focused material libraries
2. Automated degradation testing under multiple stressors
3. Feature extraction from characterization data
4. Machine learning model training and validation
5. Prediction-guided design of next-generation materials

Overcoming Data Limitations in Stability Research

The field faces several data-related challenges:

• Temporal resolution: Accelerated aging tests may not capture all degradation pathways
• Standardization: Inconsistent testing protocols hinder data comparability
• Multimodal data integration: Combining structural, electrical, and optical degradation signatures
• Sparse data regimes: Limited examples of ultra-stable compositions

Emerging Solutions to Data Challenges

The research community is addressing these limitations through:

• Open databases: Shared repositories for perovskite stability data
• Physics-informed ML models: Incorporating domain knowledge into algorithms
• Multi-fidelity modeling: Combining accelerated test data with long-term studies
• Synthetic data generation: Using simulations to augment experimental datasets

The Future of Automated Stability Optimization

The convergence of automation and artificial intelligence is transforming PSC development:

• Self-driving laboratories: Closed-loop systems for autonomous optimization
• Multi-objective optimization: Simultaneously targeting efficiency, stability, and cost
• Explainable AI models: Providing mechanistic insights beyond predictions
• Scalable manufacturing insights: Linking material stability to process parameters