Name: Circular Materials Informatics: Machine Learning for Polymer Sorting & Degradation
Start: 2026-06-27
End: 2026-06-29

About Workshop

This workshop introduces participants to the emerging field of circular materials informatics, where machine learning, data analytics, and materials science come together to improve polymer sorting, recycling, and degradation analysis. Participants will learn how AI-driven methods can support sustainable material management by identifying different polymer types, predicting degradation behavior, and enabling smarter decisions in plastic waste recycling and circular economy systems.

Aim

The aim of this workshop is to provide participants with a practical understanding of how machine learning can be applied to polymer sorting, degradation prediction, and circular materials management for sustainable recycling and waste reduction.

What Participants Will Learn

To introduce the concept of circular materials informatics and its role in sustainable polymer management.
To explain how machine learning can support polymer classification, sorting, and material identification.
To demonstrate how data-driven models can be used to predict polymer degradation behavior.
To help participants understand the importance of AI in improving recycling efficiency and circular economy practices.
To provide exposure to practical workflows involving material data, feature analysis, and predictive modeling.

Structure

📅 Day 1: Circular Materials Informatics & Polymer Data Basics

Core Objective: Understand how polymer data and AI support sustainable recycling and circular material management.

Introduction to circular materials informatics
Polymer waste types: PET, PP, PE, PVC, PS, PLA
Key polymer properties and recycling challenges
Using MDPI research insights for polymer data understanding
Basics of ML workflow for polymer informatics

🛠️ Hands-on Activity:

Polymer Data Explorer: Clean, visualize, and explore a sample polymer dataset in Google Colab.

🧰 Tools Covered: Google Colab, Python, Pandas, Matplotlib

📅 Day 2: Machine Learning for Polymer Sorting

Core Objective: Build ML models to classify polymers for smart recycling and automated sorting.

Polymer sorting methods: spectroscopy, sensors, computer vision
ML models for polymer classification
Feature selection for sorting datasets
Model evaluation using accuracy and confusion matrix
Applications in recycling plants and smart waste systems

🛠️ Hands-on Activity:

AI Polymer Sorting Classifier: Train a simple ML model to classify PET, PP, PE, PVC, and PS.

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

📅 Day 3: Polymer Degradation Prediction

Core Objective: Use ML to predict polymer degradation and support circular material decisions.

Types of polymer degradation: thermal, UV, oxidative, biodegradation
Key factors: temperature, humidity, UV exposure, additives
Regression models for degradation prediction
Predicting degradation percentage and material lifetime
Applications in recycling, reuse, and sustainable material design

🛠️ Hands-on Activity:

Polymer Degradation Predictor: Build a regression model to predict polymer degradation percentage.

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

Important Dates

Registration Ends

4:30 PM IST

Workshop Dates

2026-06-27

05:30PM IST

What You Will Gain

Outcomes

Understand the fundamentals of circular materials informatics.
Explain the role of machine learning in polymer sorting and recycling.
Identify key data types used in polymer classification and degradation studies.
Understand how predictive models can support polymer degradation analysis.
Recognize the real-world applications of AI in plastic waste management and sustainable materials development.
Apply basic machine learning thinking to circular economy and materials science challenges.

Who Should Attend

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Researchers in polymer science, materials science, recycling, and sustainability
PhD scholars and postgraduate students
Academicians and faculty members
Industry professionals from recycling, plastics, packaging, and manufacturing sectors
Sustainability and circular economy professionals
Data science and ML learners interested in materials applications
Chemical, polymer, and materials engineers
Startups working on smart recycling and sustainable materials