AI-Driven Nanomaterials Design for Energy Storage, Biosensing & Biomedical Applications
AI-Driven Nanomaterials Design for Energy Storage, Biosensing & Biomedical Applications is an Intermediate-level, 3 Days online program by NSTC.
- ModeRecorded Lectures
- LevelIntermediate
- Duration3 Days (60-90 mins per day)
| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Intermediate |
| Duration | 3 Days (60-90 mins per day) |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | Materials Project NanoHUB Citrination Google Sheets Orange Data Mining Weka |
About the AI-Driven Nanomaterials Design for Energy Storage, Biosensing & Biomedical Applications Course
This international course explores the growing role of AI in nanomaterials research.
Participants will discover how machine learning, materials informatics, and data‑driven modeling accelerate nanomaterial discovery and boost performance for energy storage, biosensing, and biomedical applications.
Program Highlights
• Comprehensive coverage of Driven Nanomaterials Design for Energy Storage from fundamentals to advanced applications
• Hands-on projects and real-world case studies in nanotechnology
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Materials Project, NanoHUB, Citrination, Google Sheets
• Career-oriented training for academic and professional growth in nanotechnology
Course Curriculum
Module 1: Day 1 – Materials Informatics & AI‑Based Property Screening
- Introduce AI and materials informatics concepts for nanomaterials
- Identify key nanomaterial descriptors and curate AI‑ready datasets
- Perform property prediction vs. screening vs. inverse design exercises
Module 2: Day 2 – Machine Learning for Energy Storage & Biosensing Nanomaterials
- Build classification and regression workflows for battery and sensor datasets
- Select impactful features and evaluate model performance
- Translate research‑paper data into reproducible AI design pipelines
Module 3: Day 3 – Biomedical Nanomaterials, Toxicity Prediction & Future Trends
- Apply AI to predict nanoparticle toxicity and drug‑release profiles
- Explore generative AI and inverse design for biomedical nanomaterials
- Discuss autonomous labs, digital twins, and emerging AI‑nano research directions
