Predictive ML of Bio-Sourced Polymers for Circular Economy Infrastructure

Aim

Workshop Objectives

Workshop Structure

📅 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

Who Should Enrol?

Important Dates

Workshop Outcomes