Circular Materials Informatics: Machine Learning for Polymer Sorting & Degradation
Automating waste processing stream logic and lifecycle modeling for recycled engineering materials
Skills you will gain:
About Program:
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.
Program Objectives:
- 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.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
Career Supporting Skills
Program 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.