This workshop introduces participants to the emerging field of polymer informatics with a focus on machine learning techniques. Polymer informatics utilizes computational and data-driven approaches to understand and predict polymer properties and behaviors, which is essential for materials innovation. The course will cover foundational concepts in polymer science and machine learning, emphasizing the integration of these disciplines. Participants will learn how to apply various machine learning models, including regression, classification, and neural networks, to solve real-world problems in polymer science and engineering. The course will address data collection, feature selection, model training, and evaluation, specifically tailored to the unique challenges of polymer datasets. Hands-on sessions will guide attendees through the process of building and deploying models using open-source tools and libraries. The course aims to equip researchers, engineers, and data scientists with the skills needed to leverage machine learning in the development of new polymers or plastics and the optimization of existing ones. By the end of the course, participants will have a solid understanding of how machine learning can drive innovation in polymer science and plastic engineering, contributing to advancements in areas such as sustainable materials, biomedical devices, compounding, additives and high-performance polymers.

This workshop is designed for professionals working in polymer development, formulation, and data-driven materials innovation, particularly those involved in creating or improving materials, including:

• Materials scientists and polymer engineers
• Data scientists and analysts working with polymer datasets
• R&D engineers developing new polymers, compounds, and formulations
• Chemical engineers and formulation specialists
• Process engineers supporting material scale-up and formulation optimization
• Graduate students and researchers (MSc, PhD) in polymer science or data-driven materials design
• Professionals interested in applying machine learning to polymer, formulation, and compound development

Polymer development is shifting from trial-and-error experimentation to data-driven design. Machine learning provides a powerful approach to accelerate the development of new polymers, enable efficient formulation design, and optimize compounds with fewer iterations.

This workshop introduces how AI and machine learning can be applied to design new polymer systems, develop optimized formulations, and create advanced compounds, providing a practical pathway to faster and more efficient materials innovation.