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.
Skills you will gain:
About Program:
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.
Program 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- 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.
Career Supporting Skills
Program 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.