AI-Driven Design of Nanomaterials for Energy Storage

Aim

Workshop Objectives

Workshop 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

🛠️ Hands-on Activity:
Research Trend Mining from Nanomaterials Literature Using Python

Participants will use Python in Google Colab to analyze sample MDPI-inspired research data, clean article titles and keywords, extract trending research terms, and visualize major themes using word clouds and frequency plots.

📅 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

🛠️ Hands-on Activity:
Predicting Energy Storage Performance of Nanomaterials

Participants will build a simple machine learning model in Google Colab to predict specific capacitance or battery capacity using sample nanomaterial features such as surface area, conductivity, pore size, and synthesis temperature.

📅 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

🛠️ Hands-on Activity:
AI-Based Screening and Ranking of Nanomaterials

Participants will create a Google Colab notebook to rank candidate nanomaterials using performance, conductivity, stability, cost, and sustainability parameters, and generate a final ranked list of suitable materials for energy storage applications.

Who Should Enrol?

Important Dates

Workshop Outcomes