Programme Parameters
Graduate / Intermediate
3 Days (4.5 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 optimization of solar cells through bandgap engineering, doping profiles, and layer compositions. Across 4 Weeks, you will build practical fluency in bandgap engineering and doping profiles, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course focuses on AI-driven optimization of solar cells through bandgap engineering, doping profiles, and layer compositions.
📋 Course Objectives
1. Build practical fluency in bandgap engineering.
2. Gain working command of doping profiles.
3. Apply biotechnology methods to authentic research and industry problems.
4. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into bandgap engineering
🚀 Key Learning Outcomes
• Confidence to apply bandgap engineering in real projects.
• Confidence to implement doping profiles in real projects.
• A demonstrable biotechnology project for your research or industry portfolio.
• 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
Photovoltaic Fundamentals and Loss Analysis
• Semiconductor band structure, absorption and carrier transport in a solar cell
• The Shockley–Queisser limit and where real devices lose against it
• J–V and external quantum efficiency measurement and what each reveals
• Systematic loss analysis: optical, recombination and resistive contributions
Device and Optical Modelling
• Drift-diffusion device simulation in SCAPS-1D and comparable tools
• Transfer-matrix optical modelling of layer stacks
• Where density functional theory helps in absorber screening, and its cost
• Architectures compared: silicon, thin film, perovskite and tandem stacks
Materials Datasets and Featurisation
• Materials Project, NOMAD and experimental databases: coverage and bias
• Composition and structure descriptors, including Magpie and SOAP representations
• Dataset curation, duplicate handling and the leakage introduced by random splits
Property Prediction and Uncertainty
• Regression models for bandgap, efficiency and stability endpoints
• Graph neural networks on crystal structures, including CGCNN-style models
• Uncertainty quantification and why a point prediction is not enough for screening
• Active learning to place the next experiment where it is most informative
Optimisation Toward Target Properties
• Bayesian optimisation over composition and process space
• Evolutionary and generative approaches to candidate proposal
• Multi-objective optimisation across efficiency, stability and cost
• Constraining the search to what can actually be synthesised
Experimental Validation and Stability
• High-throughput screening and self-driving laboratory workflows
• Degradation modelling and accelerated ageing under ISOS protocols
• Reconciling predicted with measured performance and diagnosing the gap
• Techno-economic assessment: efficiency gains that do not survive contact with cost
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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