New Year Offer End Date: 30th April 2024
Program

AI-Driven Design of Nanomaterials for Energy Storage

AI-Powered Nanomaterials for Smarter, Safer, and Sustainable Energy Storage

Skills you will gain:

About Program:

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.

Program Objectives:

  • 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.

What you will learn?

📅 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.

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

Intended For :

  • Researchers in nanotechnology, materials science, batteries, and supercapacitors
  • PhD scholars and academicians working on energy storage materials
  • Industry professionals in clean energy, battery R&D, and advanced materials
  • AI/ML professionals interested in materials science applications
  • Students and early-career professionals exploring AI-driven nanomaterial design
  • Anyone interested in sustainable energy storage and data-driven materials discovery

Career Supporting Skills

Program 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.

FREEDOM TO LEARN 10% OFF All Courses & Workshops Use Code: NANOINDIA10 ⏳ Offer Ends In: Loading... Learn Today. Lead Tomorrow. Explore Programs →
FREEDOM TO LEARN 10% OFF All Courses & Workshops Use Code: NANOINDIA10 ⏳ Offer Ends In: Loading... Learn Today. Lead Tomorrow. Explore Programs →