AI-Driven Polymer Composites: Microstructure Analysis, Property Prediction & Inverse Design
From Microscopy-Based Characterization to Machine Learning and Intelligent Composite Materials Design
Skills you will gain:
About Workshop:
This 3-day hands-on workshop explores the application of artificial intelligence and machine learning in polymer composite research, focusing on microstructural characterization, mechanical and thermal property prediction, and inverse materials design. Participants will learn to extract quantitative features from microscopy images, develop predictive machine learning models, and identify promising composite formulations that meet targeted performance requirements using Python-based computational tools.
Aim: To equip participants with practical skills in AI-driven microstructure analysis, predictive modeling, and inverse design of polymer composites for advanced materials research and engineering applications.
Workshop Objectives:
- Understand polymer composites, fillers, reinforcements and microstructure–property relationships.
- Analyze microstructural images and extract quantitative material descriptors.
- Develop machine learning models for mechanical and thermal property prediction.
- Apply feature engineering and model evaluation techniques to composite datasets.
- Explore inverse design and optimization for targeted material properties.
- Interpret model predictions and assess candidate composite formulations.
What you will learn?
Day 0 — Software Setup & Dataset Preparation
Optional • 30 min
- Introduction to the computational workflow and datasets.
- Google Colab and Python environment setup.
- Introduction to ImageJ/Fiji for microscopy analysis.
- Access to prepared SEM images and composite property datasets.
- Verification of required libraries and notebooks.
Tools: ImageJ/Fiji, Google Colab, Python, pandas, scikit-learn.
Day 1 — Polymer Composites & AI-Based Microstructure Analysis
60–90 min
Topics Covered
- Fundamentals of polymer composites, nanocomposites and reinforced materials.
- Polymer matrices, fillers, fibers and interfacial interactions.
- Microstructure–property relationships in composite materials.
- Microstructural characterization techniques: SEM, TEM and optical microscopy.
- Identification of porosity, particle dispersion, agglomeration and fiber orientation.
- Image preprocessing, thresholding and segmentation.
- Quantitative feature extraction: filler area fraction, particle size distribution and pore characteristics.
- Preparation of microstructural descriptors for machine learning.
Hands-on 1: Microstructure Image Analysis & Feature Extraction
Analyze a sample composite microscopy image using ImageJ/Fiji and extract quantitative structural parameters for AI-based materials analysis.
Tools: ImageJ/Fiji, Python, NumPy, Matplotlib.
Day 2 — Machine Learning for Composite Property Prediction
60–90 min
Topics Covered
- Introduction to materials informatics and machine learning for polymer composites.
- Experimental dataset collection, cleaning and preprocessing.
- Feature engineering using filler loading, composition, processing conditions and microstructural features.
- Supervised regression techniques: linear regression, random forest and gradient boosting.
- Prediction of tensile strength, elastic modulus and thermal conductivity.
- Model training, testing and cross-validation.
- Performance evaluation using MAE, RMSE and R².
- Feature importance and interpretation of structure–property relationships.
Hands-on 2: AI-Based Composite Property Prediction
Train and evaluate a machine learning model using a prepared polymer composite dataset to predict a selected mechanical property and visualize actual versus predicted results.
Tools: Python, Google Colab, pandas, scikit-learn, Matplotlib.
Day 3 — AI-Driven Inverse Design & Composite Optimization
60–90 min
Topics Covered
- Fundamentals of inverse materials design and data-driven optimization.
- Forward property prediction versus target-driven inverse design.
- Defining target properties and feasible material design constraints.
- Identifying influential composition and microstructural parameters.
- Introduction to surrogate-model-based optimization.
- Optimization techniques: grid search, constrained search and Bayesian optimization concepts.
- Balancing competing material properties through multi-objective optimization.
- Ranking candidate composite formulations based on predicted performance.
- Visualizing design trade-offs and candidate performance.
- Model uncertainty, experimental validation and limitations of AI-generated materials designs.
Hands-on 3: Inverse Design of Polymer Composites
Use the trained machine learning model to identify candidate filler compositions and processing parameters that meet a specified tensile-strength target within predefined design constraints.
Tools: Python, Google Colab, scikit-learn, SciPy, Matplotlib.
Mentor Profile
Fee Plan
Important Dates
16 Nov 2026 Indian Standard Timing
16 Nov 2026 to 18 Nov 2026 Indian Standard Timing 5:30 PM
Get an e-Certificate of Participation!
Intended For :
- Materials Science, Polymer Science and Nanotechnology students.
- Mechanical, Chemical and Manufacturing Engineering researchers.
- PhD scholars and postdoctoral researchers working on composite materials.
- Faculty members and academic researchers.
- Materials characterization and industrial R&D professionals.
- AI/ML researchers interested in materials informatics.
Career Supporting Skills
Workshop Outcomes
Upon successful completion, participants will be able to:
- Extract measurable microstructural features from polymer composite microscopy images.
- Prepare and analyze datasets connecting composition, microstructure and material properties.
- Build and evaluate machine learning models for composite property prediction.
- Identify important variables influencing mechanical and thermal performance.
- Apply a basic inverse design workflow to screen candidate composite formulations.
- Visualize and interpret predicted design-performance trade-offs.
- Develop a reproducible computational workflow to support future experimental research.