About Workshop
This workshop focuses on the DFT-based electronic structure optimization of MXene-derived nanohybrids for advanced energy storage applications. Participants will gain an understanding of how computational materials science, density functional theory, and nanomaterial engineering can be used to design, analyze, and optimize MXene-based hybrid materials for high-performance batteries, supercapacitors, and next-generation energy storage systems.
Aim
The aim of this workshop is to provide participants with conceptual and practical knowledge of using Density Functional Theory (DFT) to investigate the electronic, structural, and energy-related properties of MXene-derived nanohybrids, enabling the development of efficient, stable, and high-performance materials for energy storage applications.
What Participants Will Learn
- Introduce MXenes, nanohybrids, and their relevance in energy storage.
- Explain the basics of DFT for material optimization.
- Explore electronic structure properties such as band structure, DOS, charge transfer, and adsorption behavior.
- Understand how DFT helps improve MXene-based material performance.
- Discuss applications in batteries, supercapacitors, and electrochemical energy storage.
Structure
📅 Day 1: MXenes, Nanohybrids, and Energy Storage Research Trends
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- Introduction to MXenes and MXene-derived nanohybrids
- Why MXenes are important for batteries and supercapacitors
- Structure-property relationship in 2D nanomaterials
- Surface terminations, functional groups, defects, and intercalation
- Current research trends from MDPI-based literature
- Role of DFT in predicting material stability and performance
🛠️ Hands-on Activity
MDPI Research Trend Mining using Google Colab
Participants will use Python to analyze MXene-related research titles/keywords and identify trending themes such as DFT, batteries, supercapacitors, adsorption, ion diffusion, and surface functionalization.
📅 Day 2: DFT-Based Electronic Structure Analysis of MXene Nanohybrids
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- Basics of Density Functional Theory for material researchers
- Geometry optimization and total energy calculation
- Band structure and density of states analysis
- Charge density, charge transfer, and electronic conductivity
- Adsorption energy and ion interaction in MXene surfaces
- Understanding Li, Na, Zn, and H ion storage behavior
🛠️ Hands-on Activity
Electronic Structure Visualization in Google Colab
Participants will plot and interpret sample DOS, band structure, and adsorption energy data of MXene-based materials using Python.
📅 Day 3: Optimization of MXene-Derived Nanohybrids for High-Performance Energy Storage
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- Material design strategies for improving conductivity and stability
- Doping, defect engineering, and surface functionalization
- MXene-based nanohybrids for Li-ion, Na-ion, Zn-ion batteries, and supercapacitors
- Linking DFT descriptors with energy storage performance
- AI-assisted screening of MXene nanohybrids
- Future research directions and publication-ready problem framing
🛠️ Hands-on Activity
DFT Descriptor-Based Material Ranking using Google Colab
Participants will use Python to rank MXene-derived nanohybrids based on key descriptors such as adsorption energy, band gap, conductivity indicator, and ion diffusion score.
Important Dates
Registration Ends
4:00 PM IST
Workshop Dates
2026-07-02
05:30PM IST
05:30PM IST
What You Will Gain
Outcomes
- Understand the role of MXene-derived nanohybrids in advanced energy storage.
- Explain how DFT supports electronic structure analysis and material optimization.
- Interpret key outputs such as DOS, band structure, energy levels, and adsorption energy.
- Identify structure-property relationships affecting energy storage performance.
- Apply computational insights for designing improved MXene-based nanomaterials.
Who Should Attend
- Researchers in nanomaterials, MXenes, and energy storage.
- Ph.D. scholars and postgraduate students in materials science, chemistry, physics, or nanotechnology.
- Academicians working on computational materials research.
- Industry professionals in batteries, supercapacitors, and advanced materials.
- Learners interested in DFT, Python, Google Colab, and simulation-based material design.