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
This intensive 3‑day international course empowers participants to leverage Density Functional Theory (DFT) for the design and optimization of MXene heterostructures—advanced 2D materials poised to transform EV battery performance. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive 3‑day international course empowers participants to leverage Density Functional Theory (DFT) for the design and optimization of MXene heterostructures—advanced 2D materials poised to transform EV battery performance.
📋 Course Objectives
1. Translate AI Enablement 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 Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• 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 – Introduction & DFT Fundamentals
Explore MXene basics and their role in EV batteries • Grasp core DFT theory and electronic‑structure prediction • Set up a DFT environment and run a simple MXene cell calculation
Day 2 – Advanced DFT Modeling & MXene Heterostructures
Construct and optimise MXene heterostructures • Analyse band structures, DOS, and charge‑density maps • Predict intercalation potentials and ion‑diffusion pathways
Day 3 – Application, Analysis & Optimization
Screen MXene candidates for high‑energy EV storage • Perform defect engineering and capacity prediction • Integrate computational results with experimental/industry data and explore emerging design trends
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | VASP |
| Covered Tool / Platform | Quantum ESPRESSO |
| Covered Tool / Platform | Gaussian |
| Covered Tool / Platform | VESTA |
| Covered Tool / Platform | XCrySDen |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | HPC clusters |
Frequently Asked Questions
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