Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Driven Bandgap Engineering for Efficient Solar Cells zooms in on the single most important lever for solar-cell efficiency: the bandgap. You learn why the bandgap governs how much sunlight a cell can convert, and how machine learning navigates the material design space to tune it — predicting properties of candidate materials, optimising composition and structure, and guiding toward the ideal bandgap for high efficiency. The course connects materials informatics to real photovoltaic design. You finish able to reason about AI-guided bandgap engineering. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-driven bandgap engineering for solar cells — using machine learning to design and tune material bandgaps for higher photovoltaic efficiency.
📋 Course Objectives
1. Explain how bandgap governs solar efficiency.
2. Predict material properties with machine learning.
3. Optimise composition and structure for target bandgaps.
4. Navigate the photovoltaic materials design space.
5. Connect informatics to solar-cell design.
👥 Who Should Enroll?
• Materials scientists and PV researchers
• Data scientists in materials
• Renewable-energy R&D professionals
• Students of photovoltaics
🚀 Key Learning Outcomes
• An understanding of AI bandgap engineering.
• A photovoltaic materials-design perspective.
• A materials-informatics project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Aidriven Bandgap Engineering Foundations
Develop a comprehensive understanding of artificial intelligence and machine learning fundamentals, including supervised and unsupervised learning techniques • Analyze the mathematical foundations of bandgap engineering, including quantum mechanics and solid-state physics • Design and implement simple AI models using Python and popular libraries such as NumPy and SciPy
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for bandgap engineering applications, including data cleaning, preprocessing, and feature extraction • Evaluate the performance of different data preprocessing techniques, including normalization, feature scaling, and encoding • Implement data pipelines using popular tools such as Apache Beam and AWS Data Pipeline
Model Architecture, Algorithm Design, and Aidriven Bandgap Engineering Methods
Design and implement deep learning models for bandgap engineering applications, including convolutional neural networks and recurrent neural networks • Analyze the performance of different algorithmic techniques, including gradient descent and stochastic gradient descent • Develop and evaluate custom model architectures using popular frameworks such as TensorFlow and PyTorch
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate AI models using popular frameworks such as scikit-learn and Keras • Implement hyperparameter optimization techniques, including grid search and random search • Evaluate the performance of AI models using metrics such as accuracy, precision, and recall
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments using popular tools such as Docker and Kubernetes • Implement continuous integration and continuous deployment pipelines using popular tools such as Jenkins and GitLab CI/CD • Develop and evaluate MLOps workflows using popular frameworks such as TensorFlow Extended and MLflow
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI systems, including bias, fairness, and transparency • Develop and implement strategies for mitigating bias in AI systems, including data preprocessing and model regularization • Evaluate the performance of AI systems using metrics such as fairness and transparency
Industry Integration, Business Applications, and Case Studies
Develop and evaluate AI solutions for real-world industry applications, including energy and materials science • Analyze the business implications of AI systems, including cost-benefit analysis and return on investment • Implement AI solutions in industry partnerships and collaborations, including joint research and development projects
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | SciPy |
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