Materials Informatics Course | AI & ML for Materials Science | IISc
Course Details
| Exam Registration | 129 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Metallurgy and Material science & Mining Engineering, Minor in Metallurgy, Minor in Materials Science |
| Credit Points | 3 |
| Level | Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 19 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Materials Informatics: The AI Revolution in Materials Discovery
The field of materials science is undergoing a profound transformation, driven by the power of artificial intelligence and machine learning. Materials Informatics stands at the forefront of this revolution, merging vast computational datasets with intelligent algorithms to accelerate the discovery and design of next-generation materials. This is particularly critical for urgent global challenges like developing advanced batteries, solar cells, and other technologies for sustainable energy.
Master Materials Informatics with an Expert from IISc Bangalore
For postgraduate students and professionals looking to lead this charge, a comprehensive 12-week course is offered by Prof. Sai Gautam Gopalakrishnan at the Indian Institute of Science (IISc), Bangalore. Prof. Gopalakrishnan, an Assistant Professor of Materials Engineering, brings exceptional expertise from his PhD at MIT and his research focused on computational and machine learning techniques for energy applications.
This course is meticulously designed to bridge the gap between traditional materials engineering and cutting-edge data science. It doesn't just teach machine learning in a vacuum; it grounds these techniques in the fundamental physics of materials, ensuring students can generate meaningful data and interpret AI-driven results effectively.
Who Should Take This Course?
INTENDED AUDIENCE: This postgraduate-level course is ideal for:
- Postgraduate students in Metallurgy, Materials Science & Engineering, and Ceramic Engineering.
- Advanced undergraduate students from related disciplines.
- Professionals in mining engineering or related fields with a minor in metallurgy or materials science.
PREREQUISITES: A background in metallurgy, materials science, or a closely related field is recommended to fully benefit from the physics-informed approach of the curriculum.
Detailed 12-Week Course Curriculum
The course layout provides a logical progression from core data science concepts to advanced materials-specific AI applications.
Weeks 1-5: Foundations of ML for Materials
- Week 1: Introduction and key terminologies in Materials Informatics.
- Week 2: Building typical regression and classification workflows.
- Week 3: Understanding and applying classical machine learning models.
- Week 4: Diving into perceptrons and the fundamentals of neural networks.
- Week 5: Advanced architectures: Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for materials data.
Weeks 6-9: Core Computational Materials Physics
- Week 6: Density Functional Theory (DFT) - The cornerstone of electronic structure calculations.
- Week 7: Molecular Dynamics (MD) - Simulating the motion and behavior of atoms over time.
- Week 8: Statistical Mechanics - Linking atomic-scale properties to macroscopic behavior.
- Week 9: Lattice models and coarse-graining techniques for efficient simulation.
Weeks 10-12: Cutting-Edge AI for Materials
- Weeks 10 & 11: Machine-Learned Interatomic Potentials - Replacing classical force fields with accurate, efficient AI models (covering classical and graph-based approaches).
- Week 12: Advanced topics exploring the future: Transfer Learning and Generative Models for novel materials design.
Essential Reference Books
The course draws upon a robust set of textbooks that are considered standards in their respective fields:
| Book Title | Author(s) | Edition |
|---|---|---|
| Computational Materials Science | June Gunn Lee | Second Edition, 2016 |
| Understanding Molecular Simulation | Daan Frenkel & Berend Smit | Second Edition, 2002 |
| Electronic Structure: Basic Theory and Practical Methods | Richard M. Martin | Second Edition, 2020 |
| Statistical Mechanics | Donald A. McQuarrie | First Edition, 2000 |
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow | Aurélien Géron | Third Edition, 2022 |
Why This Course is Essential for Future Materials Leaders
As industries from aerospace to cleantech rapidly digitize and adopt AI tools, the demand for materials scientists who are fluent in both domain knowledge and data science is skyrocketing. This course by Prof. Sai Gautam Gopalakrishnan at IISc provides the unique, integrated skill set needed to innovate in areas like energy storage and harvesting. By completing this program, you won't just learn to use AI tools—you'll understand the science behind the data, empowering you to design smarter experiments, build better models, and contribute to groundbreaking materials discoveries.
Enroll Now →