AI-Driven Nanomaterials Design for Energy Storage, Biosensing & Biomedical Applications
Design smarter nanomaterials using AI for next-generation energy, sensing, and biomedical innovations.
About This Course
This international workshop focuses on the growing role of AI in nanomaterials research. Participants will explore how machine learning, materials informatics, and data-driven modeling can accelerate nanomaterial discovery and improve application-specific performance.
The workshop connects three high-impact research areas: energy storage materials, biosensing platforms, and biomedical nanotechnology. It is designed for researchers, academicians, PhD scholars, and industry professionals who want to understand how AI can be applied to next-generation nanomaterial design and research workflows.
Aim
The aim of this workshop is to introduce participants to how artificial intelligence can support the discovery, design, screening, and optimization of nanomaterials for energy storage, biosensing, and biomedical applications.
Workshop Objectives
- To explain the role of AI in nanomaterials discovery and design.
- To introduce key nanomaterial properties used in AI-based modeling.
- To explore applications in batteries, supercapacitors, biosensors, diagnostics, drug delivery, and biomedicine.
- To demonstrate how machine learning can support material screening, property prediction, and performance optimization.
- To help participants identify research opportunities in AI-driven nanomaterials.
- To build awareness of future trends such as materials informatics, generative AI, and autonomous material discovery.
Workshop Structure
📅 Day 1: Materials Informatics and AI-Based Nanomaterial Property Screening
- Introduction to AI and materials informatics in nanomaterials research
- How machine learning accelerates nanomaterial discovery and design
- Key nanomaterial descriptors: size, shape, surface area, porosity, charge, bandgap, conductivity, stability, and morphology
- Data sources for AI-driven nanomaterials research: literature, experimental datasets, simulations, Materials Project, NanoHUB, and open repositories
- Preparing nanomaterial data for AI: descriptors, labels, missing values, and application-specific targets
- Property prediction vs material screening vs inverse design
- Research opportunities in AI-assisted nanomaterial discovery
🛠️ Hands-on:
Tool: Materials Project / NanoHUB / Citrination Demo Resources / Google Sheets
Activity: Explore nanomaterial property data, identify candidate materials, and prepare a simple AI-ready screening table for energy, biosensing, or biomedical applications.
📅 Day 2: Machine Learning for Energy Storage and Biosensing Nanomaterials
- Nanomaterials for batteries, supercapacitors, hydrogen storage, and fuel cells
- AI for predicting conductivity, stability, charge-transfer behavior, capacity, and material performance
- Nanomaterials for biosensors, wearable sensing, electrochemical sensing, and biomarker detection
- Classification and regression workflows for nanomaterial datasets
- Feature selection and descriptor importance in AI-based material prediction
- Case studies: graphene, MXenes, quantum dots, metal oxides, and carbon nanotubes
- Translating research paper data into AI-based nanomaterial design workflows
🛠️ Hands-on:
Tool: Orange Data Mining
Activity: Build a no-code ML workflow to classify or predict nanomaterial performance using sample material or sensor data.
📅 Day 3: Biomedical Nanomaterials, Toxicity Prediction and Future AI-Nano Research
- Nanomaterials in drug delivery, imaging, diagnostics, and tissue engineering
- AI for nanoparticle toxicity, biocompatibility, and drug-release prediction
- Biomedical nanomaterial design challenges: safety, reproducibility, and clinical translation
- Generative AI and inverse design for nanomaterials
- Digital twins and simulation-assisted nanomaterial development
- Autonomous labs and self-driving materials discovery
- Future trends: AI-nano-bio convergence, explainable AI, and research automation
- How to frame an AI-nanomaterials research problem for thesis, proposal, or publication
🛠️ Hands-on:
Tool: Weka / Orange Data Mining
Activity: Create a simple no-code model to predict nanoparticle toxicity or biomedical suitability using sample descriptors.
Who Should Enrol?
- PhD scholars and research scholars
- Faculty members and academicians
- Scientists and research professionals
- Industry professionals working in materials, biotech, energy, healthcare, or nanotechnology
- Students from nanotechnology, biotechnology, biomedical engineering, materials science, chemistry, physics, chemical engineering, AI/ML, and related fields
Important Dates
Registration Ends
June 11, 2026
IST 4 : 30 PM
Workshop Dates
June 11, 2026 – June 13, 2026
IST 05:30 PM
Workshop Outcomes
- Understand the fundamentals of AI-driven nanomaterial design.
- Identify important nanomaterial properties for energy, biosensing, and biomedical applications.
- Explain how AI can assist in material selection, prediction, and optimization.
- Interpret basic nanomaterial datasets for research and application development.
- Connect AI-based approaches with real-world nanotechnology applications.
- Explore publishable research directions in AI, nanomaterials, energy storage, biosensing, and biomedicine.
Fee Structure
Student Fee
₹2499 | $60
Ph.D. Scholar / Researcher Fee
₹3499 | $70
Academician / Faculty Fee
₹4499 | $80
Industry Professional Fee
₹6499 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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