AI-Driven Bandgap Engineering for Efficient Solar Cells

• Understanding the Role of Bandgap Engineering in Solar Cells
• Recent Advances in Solar Cell Technologies: Perovskites, Silicon, and Thin-Film Solar Cells
• Overview of AI and Machine Learning in Solar Energy Research
• Introduction to Doping Profiles and Layer Composition
• Hands-on: Accessing Solar Cell Data from Open Databases (e.g., NREL, Perovskite Database)
• Hands-on: Extracting and Preprocessing Material Properties for ML Models

• Machine Learning Techniques for Bandgap Prediction: Regression and Classification Models
• Feature Engineering: Key Descriptors for Doping Profiles and Layer Compositions
• Hands-on: Building and Training Predictive Models for Bandgap Optimization
• Model Evaluation: Metrics, Validation, and Cross-Validation Techniques
• Hands-on: Optimizing Doping Profiles Using ML Algorithms for Enhanced Solar Cell Efficiency

• Deep Learning Models for Solar Cell Property Prediction: Neural Networks and CNNs
• Exploring AI Tools for Doping Optimization in Perovskite and Silicon Solar Cells
• Hands-on: Implementing Pretrained Deep Learning Models for Solar Cell Bandgap Engineering
• Understanding the Impact of Layer Composition on Light Absorption Efficiency
• Hands-on: Predicting Layer Composition and its Impact on Solar Cell Performance
• Future Research Directions: Challenges and Opportunities in AI-Driven Solar Cell Optimization

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