Predictive ML of Bio-Sourced Polymers for Circular Economy Infrastructure

• Understand the principles of bio-sourced polymers and circular economy frameworks.
• Extract, clean, and structure research-based polymer datasets for machine learning.
• Apply feature engineering techniques tailored to polymer property prediction.
• Build and evaluate predictive ML models for mechanical, thermal, and biodegradability properties.
• Interpret model outputs using feature importance and interpretable ML techniques.
• Integrate ML predictions into circular economy decision-making for sustainable material selection.
• Rank polymers for real-world applications using multi-objective analysis.
• Develop AI-assisted recommendation systems to accelerate research and industrial innovation.

📅 Day 1: Research Foundations & Data Preparation

• Overview of bio-sourced and biodegradable polymers
• Circular economy principles for sustainable materials
• Key polymer properties: mechanical, thermal, biodegradability
• Extracting research-paper data for ML
• Data cleaning, handling missing values, and structuring datasets
• Feature engineering for polymer performance prediction

🛠️ Hands-on:

• Hands-on 1: Build a polymer property dataset from sample research data in Colab
• Hands-on 2: Exploratory Data Analysis – visualize trends, correlations, and outliers

📅 Day 2: Predictive Machine Learning for Polymer Properties

• ML workflow for materials science research
• Regression models: Linear Regression, Random Forest, Gradient Boosting
• Predicting polymer mechanical and thermal properties
• Predicting biodegradability and circularity potential
• Model evaluation: MAE, RMSE, R²
• Feature importance & interpretable ML for research insights
• Avoiding overfitting and working with small datasets

🛠️ Hands-on:

• Hands-on 1: Predict tensile strength and thermal properties using a sample polymer dataset
• Hands-on 2: Compare ML models and interpret feature importance for research relevance

📅 Day 3: Circular Economy Applications & Decision Models

• Integrating predictive ML with circular economy decision-making
• Multi-objective analysis: performance vs sustainability trade-offs
• Ranking bio-sourced polymers for packaging, biomedical, and infrastructure applications
• AI-assisted recommendation systems for research or industry projects
• Case studies: sustainable packaging, green composites, bio-based infrastructure materials
• Research applications: designing publishable ML-informed polymer studies
• Future trends: polymer informatics, data-efficient ML, biodegradable composite design

🛠️ Hands-on:

• Hands-on 1: Build a polymer ranking model for circular economy applications
• Hands-on 2: Mini project – ML-based polymer recommendation system for real-world applications

🧰 Tools Covered: Google Colab, Python, Pandas, Scikit-learn, Matplotlib / Plotly

• Convert published research data into ML-ready datasets.
• Build and optimize predictive ML models for polymer properties.
• Apply interpretable ML methods to inform material research decisions.
• Translate model predictions into sustainable polymer selection strategies.
• Execute hands-on Colab-based projects, including polymer ranking and recommendation systems.
• Generate research insights that can be directly applied to publications or industrial R&D.
• Stay updated on emerging trends in polymer informatics, data-efficient ML, and biodegradable composites.

• PhD Scholars & Researchers working on polymers, sustainable materials, circular economy, or materials informatics.
• Academicians & Faculty teaching or supervising research in materials science, chemistry, or environmental engineering.
• Industry Professionals involved in polymer R&D, sustainable packaging, biomedical materials, or circular economy initiatives.

• Hands-on Datasets & Notebooks for polymer ML projects
• Predictive ML Models for mechanical, thermal, and biodegradability properties
• Feature Engineering & Data Analysis Scripts for exploration and visualization
• Circular Economy Polymer Ranking Tools for sustainable material selection
• Mini Project: ML-based polymer recommendation system
• Case Study Insights on packaging, biomedical, and infrastructure polymers
• Workshop Resource Pack: slides, datasets, reference papers
• e-Certificate of Participation