AI-Driven Microstructural Characterization, ML & Optimization of Smart Polymer Composites
Transforming Smart Polymer Composites Through AI-Powered Characterization, Predictive Modeling, and Intelligent Materials Design.
About This Course
This workshop explores the integration of artificial intelligence and machine learning with smart polymer composites for microstructural analysis, material property prediction, and design optimization. Participants will learn data-driven materials research techniques through practical sessions using tools such as ImageJ/Fiji, Python, Google Colab, and scikit-learn. The workshop emphasizes hands-on learning and AI-assisted approaches to advanced materials research.
Aim
To equip participants with practical knowledge of AI-driven microstructural characterization, machine learning-based materials property prediction, and intelligent optimization of smart polymer composites using computational tools and data-driven research workflows.
Workshop Objectives
-
Understand smart polymers, polymer blends, composite materials, and their microstructural features.
-
Learn microstructure image analysis and quantitative feature extraction using ImageJ/Fiji and Python.
-
Apply machine learning algorithms to predict mechanical, thermal, and functional properties of polymer composites.
-
Develop skills in dataset preprocessing, feature engineering, model training, and performance evaluation.
-
Explore AI-driven optimization techniques for identifying promising material compositions and processing conditions.
-
Interpret model predictions using explainable AI and assess uncertainty, reproducibility, and validation requirements.
Workshop Structure
Day 1: Smart Polymer Composites and Microstructural Characterization
Topics covered
-
Fundamentals of smart polymers, polymer blends, and composite materials.
-
Types of smart polymers: shape-memory, self-healing, and stimuli-responsive materials.
-
Microstructural features: porosity, morphology, particle dispersion, and fiber orientation.
-
Microstructure–property relationships in mechanical, thermal, and functional performance.
-
Characterization techniques: SEM, TEM, and AFM.
-
Image processing, segmentation, and quantitative microstructure analysis.
-
Introduction to AI-assisted characterization and materials informatics.
Hands-on 1: Microstructure Image Analysis and Feature Extraction
– Analyze microscopy images using ImageJ/Fiji and extract key structural features using Python.
Tools: ImageJ/Fiji, Python, Google Colab, pandas, NumPy, Matplotlib.
Day 2: Machine Learning for Materials Property Prediction
Topics covered
-
Applications of AI and machine learning in polymer-composite research.
-
Experimental dataset collection, cleaning, and preprocessing.
-
Feature engineering using material composition, processing conditions, and microstructural parameters.
-
Supervised learning, regression, decision trees, random forests, and gradient boosting.
-
Prediction of tensile strength, elastic modulus, thermal conductivity, and other material properties.
-
Model training, testing, cross-validation, and prevention of data leakage.
-
Model evaluation using MAE, RMSE, and (R^2).
-
Feature selection, importance analysis, and interpretation of predicted properties.
Hands-on 2: Machine-Learning Model Development
– Train and evaluate a predictive model using an experimental polymer-composite dataset and visualize predicted versus actual properties.
Tools: Python, Google Colab, pandas, NumPy, scikit-learn, XGBoost, Matplotlib.
Day 3: AI-Driven Microstructural Optimization and Materials Design
Topics covered
-
Fundamentals of AI-driven materials design and microstructural optimization.
-
Identification of influential material composition and processing parameters.
-
Hyperparameter tuning and predictive model improvement.
-
Bayesian optimization and intelligent design-space exploration.
-
Multi-objective optimization of strength, stiffness, thermal performance, and flexibility.
-
Model interpretability using feature-importance and explainable AI techniques.
-
Comparison of candidate material designs and performance trade-offs.
-
Integration of predictive modeling with optimization workflows.
-
Experimental validation, uncertainty, reproducibility, and limitations of AI-based materials design.
Hands-on 3: AI-Based Composite Design Optimization
– Apply an optimization method to a trained predictive model, identify promising material parameters, and visualize candidate performance.
Tools: Optuna, SHAP, scikit-optimize, scikit-learn, Google Colab, Plotly.
Who Should Enrol?
-
Undergraduate and postgraduate students in Nanotechnology, Materials Science, Polymer Science, Chemical Engineering, Mechanical Engineering, Chemistry, Physics, and related disciplines.
-
PhD scholars and research fellows working on polymers, composites, advanced materials, and materials characterization.
-
Faculty members, academic researchers, and R&D professionals interested in AI-assisted materials research.
-
Beginners interested in AI for materials science, provided they have a basic understanding of scientific concepts.
Important Dates
Registration Ends
October 27, 2026
IST 4:30 PM
Workshop Dates
October 27, 2026 – October 29, 2026
IST 5:00 PM
Workshop Outcomes
-
Analyze microscopy images and extract meaningful microstructural parameters.
-
Prepare experimental materials datasets for machine learning applications.
-
Train and evaluate predictive models using Python and scikit-learn.
-
Interpret model performance using MAE, RMSE, and (R^2).
-
Apply optimization methods to identify candidate polymer-composite designs.
-
Visualize predicted material properties and compare performance trade-offs.
-
Understand the limitations of AI-based materials predictions and the importance of experimental validation.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $115
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
View All Feedbacks →