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AI for Next-Generation Semiconductor Material Discovery
Original price was: INR ₹4,999.00.INR ₹2,499.00Current price is: INR ₹2,499.00.
Using ML Algorithms to Predict Properties of Novel Materials like GaN, SiC, or 2D Semiconductors for High-Performance Applications.
About This Course
This course delves into the exciting intersection of AI, machine learning (ML), and semiconductor material discovery, focusing on next-generation materials like GaN, SiC, and 2D materials. Participants will gain hands-on experience in extracting data, predicting material properties, and applying deep learning techniques to accelerate material discovery.
Aim
The course aims to teach AI and ML techniques to predict the properties of semiconductor materials such as GaN, SiC, and 2D materials. Emphasis will be placed on data extraction, property prediction, and the use of deep learning applications in material discovery.
Course Structure
📅 Module 1 – Foundations of AI in Materials Science
- Introduction to AI/ML in Semiconductor Materials: Learn how AI and ML are transforming the field of semiconductor materials research.
- Emerging Trends in Materials Informatics: Explore the latest research trends and developments in materials informatics and computational materials science.
- Overview of Semiconductor Materials: Get to know key materials like GaN, SiC, and 2D materials and their significance in the semiconductor industry.
- Accessing and Extracting Data: Learn how to access and extract data from materials science databases like Materials Project, AFLOW, and OQMD.
- Hands-on: Build initial datasets for machine learning using Python and Matminer.
📅 Module 2 – Machine Learning for Property Prediction
- ML Algorithms for Property Prediction: Dive into machine learning algorithms for predicting key material properties such as bandgap, stability, and thermal conductivity.
- Descriptor Engineering: Learn how to create atomic, structural, and electronic features for your models.
- Hands-on: Extract features and train models using Scikit-learn.
- Model Evaluation: Understand how to evaluate models using metrics like MAE, RMSE, and R².
- Hands-on: Visualize and interpret machine learning predictions.
📅 Module 3 – Deep Learning and Advanced AI Tools
- Deep Learning Models in Materials Science: Get an introduction to advanced deep learning models like CGCNN, MEGNet, and ALIGNN, which are designed for materials discovery.
- Hands-on: Use pretrained deep learning models to predict material properties.
- AI Applications in 2D Materials Discovery: Explore how AI is used in the discovery of emerging 2D semiconductor materials.
- Hands-on: Predict the properties of 2D materials using deep learning.
- Discussion: Look ahead at the future of materials AI, research challenges, and the development of new tools.
Who Should Enrol?
- Researchers and Professionals: In materials science, semiconductor technology, and AI/ML who want to apply AI to material discovery and property prediction.
- Students: With an interest in semiconductor materials, machine learning, and AI applications in materials discovery.
- Individuals Interested in Semiconductor Material Discovery: Looking to learn how AI techniques can be used to predict properties and accelerate material discovery.
- Those with a Basic Understanding of Machine Learning, Python, and Semiconductor Technologies: Who want to deepen their knowledge and practical skills in semiconductor material optimization.
Course Outcomes
- Proficiency in AI/ML Techniques: Learn how to apply AI and ML algorithms to predict material properties in semiconductor research.
- Hands-on Experience: Gain practical experience in data extraction, model development, and machine learning workflows.
- Understanding Advanced AI Tools: Become familiar with cutting-edge deep learning models like CGCNN, MEGNet, and ALIGNN used in semiconductor material discovery.
- Enhanced Research Skills: Develop the ability to evaluate and interpret ML models for material property predictions.
- Career Readiness: Gain the knowledge and skills needed to pursue a career in materials science, AI research, or semiconductor technology.
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