AI-Driven Bandgap Engineering for Efficient Solar Cells
Predictive Models Optimizing Doping Profiles and Layer Compositions for Enhanced Light Absorption
About This Course
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.
Workshop 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.
Workshop Structure
📅 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
Who Should Enrol?
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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
Important Dates
Registration Ends
03/16/2026
IST 4:30
Workshop Dates
03/16/2026 – 03/18/2026
IST 5:30 PM
Workshop 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.
Meet Your Mentor(s)
Fee Structure
Student
₹1999 | $60
Ph.D. Scholar / Researcher
₹2999 | $70
Academician / Faculty
₹3999 | $80
Industry Professional
₹5999 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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