AI-Driven Bandgap Engineering for Efficient Solar Cells

This workshop focuses on AI-driven optimization of solar cells through bandgap engineering, doping profiles, and layer compositions. Participants will learn machine learning and deep learning techniques to enhance solar cell efficiency, with hands-on sessions using Python, TensorFlow, and open solar datasets.

• To understand the role of AI in optimizing solar cell performance.

• To explore machine learning techniques for bandgap prediction and doping optimization.

• To gain hands-on experience with solar cell data and AI tools.

• To learn how to apply deep learning models for solar cell efficiency enhancement.

• To develop skills in using Python, TensorFlow, and Scikit-learn for solar energy applications.

📅 Day 1 – Introduction to AI in Solar Cell Optimization

• 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

📅 Day 2 – Machine Learning for Bandgap Prediction and Optimization

• 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

📅 Day 3 – Deep Learning and Advanced AI for 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

• Researchers and professionals in renewable energy, material science, and AI

• Students with an interest in solar energy and AI applications

• Individuals seeking to apply AI techniques to optimize solar cell performance

• Those with a basic understanding of machine learning, Python, and solar cell technologies

• Proficiency in AI techniques for optimizing solar cell performance.

• Skills in bandgap engineering, doping profiles, and layer composition optimization.

• Experience with Python, TensorFlow, and Scikit-learn for solar applications.

• Hands-on practice with open solar datasets.

• Understanding of deep learning models for solar energy research.