This workshop explores the integration of machine learning and data science in materials research, equipping participants with methodologies to analyze complex datasets, predict material properties, and drive innovation in next-generation materials development.
Aim
This workshop aims to bridge the gap between materials science and artificial intelligence by enabling participants to leverage data and machine learning for faster, smarter, and more efficient materials innovation.
What Participants Will Learn
Introduce the fundamentals of data-driven materials discovery.
Explain the role of machine learning in predicting material properties.
Develop skills in handling and analyzing materials datasets.
Provide practical exposure to ML tools for materials research.
Enable faster and smarter discovery of novel materials.
Structure
📅 1:Foundations + Data Understanding
Understand materials data and build the first ML model
Introduction to materials informatics
Types of materials data:
Introduction to a real-world materials dataset
Hands-on Activities
Load dataset in Google Colab
Data cleaning and preprocessing
Feature understanding: composition to features
📅 Day 2: Machine Learning for Property Prediction
Build predictive models for material properties
Regression models for materials discovery
Linear Regression
Random Forest
Basics of feature engineering
Hands-on Activities
Train an ML model to predict material properties
Example targets: bandgap, conductivity, or strength
Evaluate model performance using R² and MAE
📅 Day 3: Optimization + Interpretation + Research Output
Make results research-ready
Model improvement techniques
Feature importance analysis
Interpretation of results
Hands-on Activities
Improve model performance
Generate plots and comparison graphs
Export results for reporting
Final Output
Model, results, plots, and a research-ready case study
🧰 Tools Used
Python
Google Colab
Pandas
Scikit-learn
Excel (optional for quick analysis)
Important Dates
Registration Ends
4:00 PM IST
Workshop Dates
2026-04-08
05:30PM IST
05:30PM IST
What You Will Gain
Outcomes
Understand key concepts of data-driven materials discovery.
Apply machine learning techniques to materials datasets.
Build basic predictive models for material properties.
Analyze and interpret data for informed materials design.
Gain practical skills for AI-driven materials research.
Who Should Attend
Students in Materials Science, Chemistry, Physics, and Engineering
Ph.D. scholars and researchers in materials-related fields
Academicians and faculty members
Industry professionals in materials R&D and product development
Data science and AI/ML learners interested in materials applications
