| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Intermediate |
| Duration | 3 Days (60-90 Minutes each day) |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | VASP Quantum ESPRESSO Gaussian VESTA XCrySDen Python Matplotlib NumPy Pandas HPC clusters |
About the Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials 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.
Explore electronic, structural, and electrochemical properties, master ion‑intercalation modelling, and translate computational insights into higher‑efficiency, more stable battery systems.
Program Highlights
• Comprehensive coverage of Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials from fundamentals to advanced applications
• Hands-on projects and real-world case studies in materials science
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: VASP, Quantum ESPRESSO, Gaussian, VESTA
• Career-oriented training for academic and professional growth in materials science
Course Curriculum
Module 1: 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
Module 2: 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
Module 3: 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
Tools, Techniques, or Platforms Covered
VASP Quantum ESPRESSO Gaussian VESTA XCrySDen Python Matplotlib NumPy Pandas HPC clusters
Real-World Applications
- Apply Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical materials science competencies
- Solve industry-relevant problems using Density Functional Theory Modeling of MXene Heterostructures for EV Battery Materials methodologies and tools
- Contribute to open-source projects and collaborative research in materials science
- Prepare for competitive examinations, interviews, and professional certifications in materials science
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and real‑world EV battery datasets
- Dedicated expert mentorship and doubt‑resolution sessions
Certification
