About Workshop
This workshop focuses on how Artificial Intelligence can accelerate the discovery, design, and optimization of nanomaterials for advanced energy storage applications. Participants will explore how AI, machine learning, and data-driven modeling can support the development of high-performance materials for batteries, supercapacitors, and next-generation sustainable energy technologies.
Aim
The aim of this workshop is to provide participants with a practical understanding of how AI-driven approaches can be used to design and analyze nanomaterials for efficient, reliable, and sustainable energy storage systems.
What Participants Will Learn
- To introduce the role of nanomaterials in modern energy storage technologies.
- To explain how AI and machine learning can support material discovery and performance prediction.
- To help participants understand data-driven methods for analyzing material properties.
- To explore applications of AI-designed nanomaterials in batteries, supercapacitors, and clean energy systems.
- To provide insights into current research trends and future opportunities in AI-enabled materials science.
Structure
📅 Day 1: Foundations of AI-Driven Nanomaterials for Energy Storage
- Introduction to nanomaterials for energy storage applications
- Role of nanomaterials in batteries, supercapacitors, and next-generation energy devices
- Key material properties: surface area, conductivity, porosity, particle size, and stability
- Importance of AI in nanomaterial discovery and design
- Basics of materials informatics and data-driven materials research
- Understanding MDPI-based research trends in nanomaterials and energy storage
📅 Day 2: Machine Learning for Nanomaterial Property Prediction
- Machine learning for nanomaterial performance prediction
- Understanding material datasets: composition, synthesis conditions, structure, and performance values
- Important input features: surface area, pore size, conductivity, synthesis temperature, and material type
- Predicting battery capacity, specific capacitance, energy density, and cycle stability
- Regression models for material property prediction
- Model evaluation using MAE, RMSE, and R² score
📅 Day 3: AI-Based Material Screening, Optimization, and Future Research Directions
- AI-based screening of candidate nanomaterials for batteries and supercapacitors
- Ranking materials based on performance, stability, cost, sustainability, and scalability
- Explainable AI for understanding important material features
- AI-assisted optimization of nanomaterial design and synthesis conditions
- Use of Generative AI and LLMs for literature analysis and research planning
- Future trends: autonomous materials discovery, smart laboratories, digital twins, and AI-guided synthesis
Important Dates
Registration Ends
4:00 PM IST
Workshop Dates
2026-07-13
05:30PM IST
05:30PM IST
What You Will Gain
Outcomes
- Understand the fundamentals of nanomaterials used in energy storage.
- Explain the importance of AI in accelerating nanomaterial design and optimization.
- Identify key material properties that influence battery and supercapacitor performance.
- Understand how machine learning models can be applied for material screening and prediction.
- Recognize real-world applications of AI-driven nanomaterials in sustainable energy storage.
- Gain awareness of emerging research directions in AI, nanotechnology, and energy materials.