AI-Driven Materials Discovery: Machine Learning for Next-Generation Materials Design
Accelerating the Future of Materials Innovation Through Artificial Intelligence, Machine Learning, and Data-Driven Discovery.
About This Course
The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming the way advanced materials are discovered, designed, and optimized. This workshop introduces participants to AI-driven materials discovery workflows, where computational approaches and data-driven models are used to predict material properties and accelerate the development of next-generation materials.
Participants will explore materials databases, machine learning techniques, property prediction approaches, and AI-assisted materials design strategies with practical hands-on exposure.
Aim
To provide participants with a comprehensive understanding of how AI and Machine Learning approaches are applied in materials science to accelerate materials discovery, property prediction, and intelligent materials design.
Workshop Objectives
- Understand the fundamentals of AI-driven materials discovery and materials informatics.
- Explore materials databases and data-driven materials research workflows.
- Learn machine learning approaches for predicting material properties.
- Understand structure–property relationships using AI-based methods.
- Apply AI workflows for materials screening and candidate identification.
- Gain practical exposure to computational tools used in modern materials research.
Workshop Structure
📅 Day 1: Fundamentals of AI-Driven Materials Discovery & Materials Informatics
Focus:
Understanding the foundation of materials data, machine learning workflows, and AI applications in materials science.
Topics Covered:
- Introduction to AI-driven materials discovery
- Evolution from traditional materials development to data-driven approaches
- Fundamentals of materials informatics
- Understanding materials datasets and their applications
- Overview of open-source materials databases: Materials Project, AFLOW, OQMD, NOMAD
- Materials representation and descriptors: Chemical composition, Crystal structures, Atomic features, Structure–property relationships
- AI workflow for materials discovery:
🛠️ Hands-on Session:
Materials Dataset Exploration & Preparation
Participants will explore open materials databases and prepare datasets for AI-based materials analysis.
Tools Covered:
Materials Project, Python, Google Colab, Matminer
📅 Day 2: Machine Learning for Materials Property Prediction
Focus:
Building machine learning models to predict material properties and understand AI-based material behaviour analysis.
Topics Covered:
- Fundamentals of machine learning for materials science
- Data preprocessing and feature engineering
- Generating materials descriptors
- Machine learning algorithms: Linear Regression, Random Forest, Gradient Boosting, Neural Networks
- Predicting material properties: Electronic properties, Thermal properties, Mechanical properties, Stability and performance indicators
- Model evaluation and optimization: Training and testing, Cross-validation, Performance metrics
- Introduction to Explainable AI (XAI) for materials prediction
🛠️ Hands-on Session:
Building an AI Model for Materials Property Prediction
Participants will train and evaluate machine learning models using real materials datasets.
Tools Covered:
Python, Scikit-learn, Matminer, Jupyter Notebook, SHAP
📅 Day 3: Advanced AI-Based Materials Design & Future Discovery
Focus:
Exploring advanced AI strategies for designing next-generation materials.
Topics Covered:
- AI-assisted materials screening and candidate selection
- High-throughput materials discovery workflows
- Introduction to computational materials modelling: Density Functional Theory (DFT), Molecular Dynamics, Machine Learning Interatomic Potentials
- Advanced AI approaches: Graph Neural Networks (GNNs), Generative AI for materials design, Physics-informed machine learning
- Applications of AI-designed materials: Battery materials, Thermoelectric materials, Semiconductor materials,2D materials, Functional nanomaterials
- Future trends: Autonomous materials discovery, AI laboratories,Digital materials design platforms
🛠️ Hands-on Session:
AI-Based Materials Screening Workflow
Participants will apply AI-based approaches to identify potential material candidates based on desired properties.
Tools Covered:
Materials Databases, Python ML Libraries, AI Materials Screening Platforms
Who Should Enrol?
- Undergraduate and postgraduate students in:
- Materials Science
- Nanotechnology
- Physics
- Chemistry
- Chemical Engineering
- Computational Science
- PhD scholars and researchers working in:
- Materials discovery
- Nanomaterials
- Energy materials
- Computational materials science
- AI/ML researchers interested in scientific applications.
- Industry professionals working in:
- Advanced materials
- Electronics
- Energy storage
- Materials innovation
Prerequisites:
Basic understanding of materials science or programming fundamentals is helpful but not mandatory. The workshop is designed for participants ranging from beginner to advanced levels.
Important Dates
Registration Ends
October 7, 2026
IST 4:30 PM
Workshop Dates
October 7, 2026 – October 9, 2026
IST 5:00 PM
Workshop Outcomes
✅ Understand the role of AI and ML in accelerating materials discovery.
✅ Work with materials datasets and open-source materials databases.
✅ Develop basic machine learning workflows for materials property prediction.
✅ Interpret AI-based predictions for materials design applications.
✅ Understand emerging approaches such as generative AI, computational modelling, and autonomous materials discovery.
✅ Identify opportunities for applying AI in energy, nanotechnology, semiconductor, and advanced materials research.
Fee Structure
Student
₹2499 | $65
Ph.D. Scholar / Researcher
₹3499 | $75
Academician / Faculty
₹4499 | $85
Industry Professional
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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