Computational AI for Intelligent Solar Cell Design
Predictive Models Optimizing Doping Profiles and Layer Compositions for Enhanced Light Absorption
Skills you will gain:
About Program:
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.
Aim:
The aim of this workshop is to teach participants how to use AI techniques to optimize solar cell efficiency through bandgap engineering, doping profiles, and layer compositions.
Program Objectives:
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To understand the role of AI in optimizing solar cell performance.
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To explore machine learning techniques for bandgap prediction and doping optimization.
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To gain hands-on experience with solar cell data and AI tools.
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To learn how to apply deep learning models for solar cell efficiency enhancement.
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To develop skills in using Python, TensorFlow, and Scikit-learn for solar energy applications.
What you will learn?
📅 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
Intended For :
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Researchers and professionals in renewable energy, material science, and AI
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Students with an interest in solar energy and AI applications
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Individuals seeking to apply AI techniques to optimize solar cell performance
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Those with a basic understanding of machine learning, Python, and solar cell technologies
Career Supporting Skills
Program Outcomes
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Proficiency in AI techniques for optimizing solar cell performance.
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Skills in bandgap engineering, doping profiles, and layer composition optimization.
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Experience with Python, TensorFlow, and Scikit-learn for solar applications.
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Hands-on practice with open solar datasets.
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Understanding of deep learning models for solar energy research.