AI for Next-Generation Semiconductor Material Discovery
Using ML Algorithms to Predict Properties of Novel Materials like GaN, SiC, or 2D Semiconductors for High-Performance Applications
About This Course
This workshop explores AI and ML applications in discovering next-gen semiconductor materials like GaN, SiC, and 2D materials. Participants will gain hands-on experience in data extraction, property prediction, and deep learning for material discovery.
Aim
The workshop aims to teach AI and ML techniques for predicting properties of semiconductor materials like GaN, SiC, and 2D materials, focusing on data extraction, property prediction, and deep learning applications.
Workshop Structure
📅 Day 1 – Foundations of AI in Materials Science
- Introduction to AI/ML Applications in Semiconductor Materials
- Emerging Research Trends in Materials Informatics
- Overview of Semiconductor Materials: GaN, SiC, 2D Materials
- Accessing and Extracting Data from Materials Science Databases (Materials Project, AFLOW, OQMD)
- Hands-on: Building Initial Datasets for ML Using Python & Matminer
📅 Day 2 – Machine Learning for Property Prediction
- ML Algorithms for Predicting Material Properties: Bandgap, Stability, Thermal Conductivity
- Descriptor Engineering: Atomic, Structural, and Electronic Features
- Hands-on: Feature Extraction and Model Training with Scikit-learn
- Model Evaluation Techniques: MAE, RMSE, R²
- Hands-on: Visualization and Interpretation of ML Predictions
📅 Day 3 – Deep Learning and Advanced AI Tools
- Introduction to Deep Learning Models in Materials Science: CGCNN, MEGNet, ALIGNN
- Hands-on: Using Pretrained Deep Learning Models for Property Prediction
- Exploring AI Applications in 2D Semiconductor Discovery
- Hands-on: Predicting Properties of Emerging 2D Materials
- Discussion on Future Research Directions, Tools, and Challenges in Materials AI
Who Should Enrol?
This workshop is intended for researchers, engineers, and students in materials science, semiconductor technology, and AI/ML. It is ideal for those interested in applying AI techniques to material discovery and property prediction in the semiconductor industry. Prior knowledge of Python and basic machine learning concepts is recommended but not required.
Important Dates
Registration Ends
09/30/2025
IST 4:30 PM
Workshop Dates
09/30/2025 – 10/02/2025
IST 5:30 PM
Workshop Outcomes
-
Proficiency in AI/ML Techniques: Participants will gain the skills to apply AI and ML algorithms for predicting material properties in semiconductor research.
-
Hands-on Experience: Practical experience in data extraction, preparation, and machine learning model development.
-
Understanding Advanced AI Tools: Familiarity with deep learning models like CGCNN, MEGNet, and ALIGNN for semiconductor material discovery.
-
Enhanced Research Skills: Ability to evaluate and interpret machine learning models for material property prediction.
-
Career Readiness: Equipped with the knowledge and skills to pursue careers in materials science, AI research, and semiconductor technology.
Fee Structure
Student Fee
₹1999 | $60
Ph.D. Scholar / Researcher Fee
₹2999 | $70
Academician / Faculty Fee
₹3999 | $80
Industry Professional Fee
₹5999 | $100
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
View All Feedbacks →