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AI-Driven Bandgap Engineering for Efficient Solar Cells
Original price was: USD $99.00.USD $59.00Current price is: USD $59.00.
Predictive Models Optimizing Doping Profiles and Layer Compositions for Enhanced Light Absorption
About This Course
This course focuses on optimizing solar cell efficiency using AI-driven techniques, including bandgap engineering, doping profiles, and layer compositions. Participants will explore machine learning and deep learning methods to enhance solar cell performance, with hands-on sessions using Python, TensorFlow, and open solar datasets. By the end of the course, you’ll have the skills to apply AI to the optimization of solar cells for improved efficiency.
Aim
The aim of this course is to teach participants how to apply AI techniques to optimize solar cell efficiency through bandgap engineering, doping profiles, and layer compositions.
Course Objectives
- Understand the role of AI in improving solar cell performance.
- Explore machine learning techniques for bandgap prediction and doping optimization.
- Gain hands-on experience with solar cell data and AI tools.
- Learn to apply deep learning models to enhance solar cell efficiency.
- Develop practical skills using Python, TensorFlow, and Scikit-learn for solar energy applications.
Course Structure
📅 Module 1 – Introduction to AI in Solar Cell Optimization
- Understanding the Role of Bandgap Engineering in Solar Cells: Learn how bandgap engineering contributes to solar cell efficiency.
- Recent Advances in Solar Cell Technologies: Explore innovations in perovskites, silicon, and thin-film solar cells.
- Overview of AI and Machine Learning in Solar Energy Research: Understand how AI is being used to optimize solar energy systems.
- Introduction to Doping Profiles and Layer Composition: Learn the significance of doping and layer composition in solar cell design.
- Hands-on: Access and explore solar cell data from open databases (e.g., NREL, Perovskite Database).
- Hands-on: Extract and preprocess material properties to build machine learning models.
📅 Module 2 – Machine Learning for Bandgap Prediction and Optimization
- Machine Learning Techniques for Bandgap Prediction: Understand regression and classification models for predicting bandgap properties.
- Feature Engineering: Discover key descriptors used for doping profiles and layer compositions.
- Hands-on: Build and train predictive models to optimize bandgap properties for solar cells.
- Model Evaluation: Learn about model evaluation metrics, validation, and cross-validation techniques.
- Hands-on: Use machine learning algorithms to optimize doping profiles and improve solar cell efficiency.
📅 Module 3 – Deep Learning and Advanced AI for Solar Cell Efficiency
- Deep Learning Models for Solar Cell Property Prediction: Learn how to apply neural networks and CNNs for solar cell property prediction.
- AI Tools for Doping Optimization in Perovskite and Silicon Solar Cells: Explore deep learning tools to optimize doping profiles in various solar cell types.
- Hands-on: Implement pretrained deep learning models for bandgap engineering in solar cells.
- Understanding the Impact of Layer Composition on Light Absorption Efficiency: Learn how layer composition affects solar cell performance.
- Hands-on: Predict layer composition and its impact on solar cell efficiency.
- Future Research Directions: Discuss the challenges and opportunities in AI-driven solar cell optimization.
Course Outcomes
- Proficiency in applying AI techniques to solar cell optimization, including bandgap engineering and doping profile optimization.
- Hands-on experience with solar cell data and the tools required for AI-driven solar energy applications.
- Strong understanding of deep learning models and their application to enhance solar cell efficiency.
- Ability to build machine learning models using Python, TensorFlow, and Scikit-learn for solar cell data analysis.
- Knowledge of advanced solar cell optimization techniques and their impact on efficiency improvement.
Who Should Enrol?
- Researchers and Professionals: In renewable energy, material science, and AI who want to apply AI to optimize solar cell performance.
- Students: With an interest in solar energy and AI applications.
- Individuals Interested in Solar Cell Optimization: Looking to learn how AI techniques can improve solar energy systems.
- Those with a Basic Understanding of Machine Learning, Python, and Solar Cell Technologies: Who want to deepen their knowledge and practical skills in solar cell optimization.
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