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
Explore the fundamentals of conducting polymers, sustainability principles, and AI‑driven materials informatics, then apply those concepts through hands‑on labs, real‑world case studies, and strategies to bring green polymer technologies from the lab to market. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Explore the fundamentals of conducting polymers, sustainability principles, and AI‑driven materials informatics, then apply those concepts through hands‑on labs, real‑world case studies, and strategies to bring green polymer technologies from the lab to market.
📋 Course Objectives
1. Translate AI in Sustainability & Climate theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Sustainability & Climate
• R&D engineers and working professionals applying AI in Sustainability & Climate in industry
• Academics and educators building research or teaching capacity in AI in Sustainability & Climate
🚀 Key Learning Outcomes
• A demonstrable AI in Sustainability & Climate 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
Day 1 – Foundations: Conducting Polymers, Sustainability & AI
Understand polymer fundamentals, charge transport, and green electronics applications • Analyze life‑cycle assessment, toxicity, and eco‑design standards for polymer materials • Explore AI & materials informatics basics, data sources, and workflow for polymer discovery
Day 2 – AI‑Driven Design & Optimization
Build machine‑learning models to predict conductivity, bandgap, stability, and mechanical properties • Select monomers, dopants, and greener synthesis routes using AI‑guided optimization • Integrate sustainability metrics—carbon footprint, energy use—into the design loop
Day 3 – Hands‑On Labs, Case Studies & Translation
Execute AI workflows in Google Colab/Jupyter with real polymer datasets • Interpret model outputs to make sustainable material selection decisions • Study industry case studies, IP strategy, and pathways from lab to market
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Materials Informatics Databases |
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