Data-Driven Materials Discovery Using Machine Learning
From Data to Discovery: Revolutionizing Materials Design with AI
Skills you will gain:
About Program:
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.
Program Objectives:
What you will learn?
📅 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)
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
Career Supporting Skills
Program 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.