Artificial Intelligence and Machine Learning in Materials Engineering

Week 1 :

• Introduction to the course: This will provide basic aspects of the course: why AI/ML in Materials Engineering.
• Basics of Materials Science-I: Structure of Materials
• Basics of Materials Science-II: Microstructure property correlation

Week 2 :

• Basics of Materials Science-III: Processing of materials
• Basics of Materials Science-IV: Correlation between processing with materials structure-I
• Basics of Materials Science-V: Correlation between processing with materials structure-II

Week 3 :

• Materialsdesign at different length scale - I: Basic Design Principles
• Materialsdesign at different length scale - II: Component level to atomic level aspects of material design
• Materialsdesign at different length scale - III: CALPHAD;

Week 4 :

• Materialsdesign at different length scale - IV: Ab initio, Density Functional Theory (DFT),
• Materialsdesign at different length scale - V: Monte Carlo (MC) or Molecular Dynamic (MD) followed Phase Field Simulations (PFM) for microstructural evolution.
• Machine Learning Approaches for Materials Design-I: Statistical Tools, Machine Learning-I

Week 5 :

• Machine Learning Approaches for Materials Design-II: Statistical Tools, Machine Learning,-II
• Machine Learning Approaches for Materials Design-III: Statistical Tools, Machine Learning-III
• Machine Learning Approaches for Materials Design-IV: Computer vision-I
• Machine Learning Approaches for Materials Design-V: Computer vision-II

Week 6 :

• Machine Learning Approaches for Materials Design-VI: Microstructural evolution-I
• Machine Learning Approaches for Materials Design-VII: Microstructural evolution-II
• Machine Learning Approaches for Materials Design-VIII: Microstructure property correlation-I

Week 7 :

• Machine Learning Approaches for Materials Design-IX: Microstructure property correlation-II
• Machine Learning Approaches for Materials Design-X: Microstructure property correlation-III
• Accelerating Materials Development and Deployment-I: Microstructure property correlation-IV

Week 8 :

• Accelerating Materials Development and Deployment-II: Deep Learning-I
• Accelerating Materials Development and Deployment-III: Deep Learning-II
• Accelerating Materials Development and Deployment-IV: Deep Learning-III

Week 9 :

• Accelerating Materials Development and Deployment-V: Inverse design using AI/ML from evolutionary algorithms to deep learning-I
• Materials Knowledge and Materials Data Science-I: Inverse design using AI/ML from evolutionary algorithms to deep learning-II
• Materials Knowledge and Materials Data Science-II: Inverse design using AI/ML from evolutionary algorithms to deep learning-III

Week 10 :

• Materials Knowledge and Materials Data Science-III: Advanced Deep Learning-I
• Materials Knowledge and Materials Data Science-IV: Advanced Deep Learning-I
• Materials Knowledge and Materials Data Science-V: AI/ML for materials characterization-I
• Materials Knowledge and Materials Data Science-VI: AI/ML for materials characterization-II

Week 11 :

• Materials Knowledge and Materials Data Science-VII: AI/ML for materials characterization-III
• Materials Knowledge and Materials Data Science-VIII: AI/ML for materials characterization-IV
• Materials Knowledge and Materials Data Science-VIII: AI/ML for autonomous experiments-I
• Materials Knowledge and Materials Data Science-VIII: AI/ML for autonomous experiments-II

Week 12 :

• Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-I
• Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-II
• Materials Knowledge and Materials Data Science-IX: Materials Informatics and Data Science-III
• Materials Knowledge and Materials Data Science-X: Summary and Way forward