Workshop
Artificial Intelligence and Machine Learning in Polymer Informatics
September 3, 5, 8 & 10, 2025
All workshop days are from 11:00 AM to 1:00 PM EDT.
Online
Summary
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.
Registration Information
| SPE Premium Member |
$720 |
| SPE Members |
$800 |
| Nonmembers |
$1,000 |
Register Now
Not an SPE member? Join today and attend this workshop at a discounted rate!
Workshop Packs
Strengthen your team’s skills and take advantage of group savings with an SPE Workshop Pack.
Go here for Workshop Pack information and registration.
Instructor
Ying Li, Ph.D.
Professor
University of Wisconsin - Madison
Dr. Ying Li joined the University of Wisconsin-Madison in August 2022 as an Associate Professor of Mechanical Engineering. From 2015 to 2022, he was an Assistant Professor of Mechanical Engineering at the University of Connecticut and was promoted to Associate Professor. He received his Ph.D. in 2015 from Northwestern University, focusing on the multiscale modeling of polymers and related biomedical applications. His current research interests are: multiscale modeling, computational materials design, mechanics and physics of polymers, and machine learning-accelerated polymer design. Dr. Li’s achievements in research have been widely recognized by fellowships and awards, including ACS Polymeric Material Science and Engineering (PMSE) Young Investigator Award (2023), NSF CAREER Award (2021), Air Force’s Young Investigator Award (2020), 3M Non-Tenured Faculty Award (2020), ASME Haythornthwaite Young Investigator Award (2019), NSF CISE Research Initiation Initiative Award (2018) and multiple best paper awards from major conferences. He has authored and co-authored more than 130 peer-reviewed journal articles, including Science Advances, Nature Communications, Physical Review Letters, ACS Nano, and Macromolecules, etc. He has been invited as a reviewer for more than 100 international journals, such as Nature Communications and Science Advances. Dr. Li’s lab is currently supported by multi-million-dollar grants and contracts from NSF, AFOSR, AFRL, ONR, DOE/National Nuclear Security Administration, DOE/National Alliance for Water Innovation, and industries.
Questions? Contact:
For questions, contact Iván D. López.
Who Should Attend?
- Polymer Scientists and Engineers: Professionals in polymer science and engineering who are interested in incorporating machine learning techniques into their research and development processes.
- Material Scientists: Researchers working in materials science who wish to expand their analytical toolkit with machine learning methodologies specific to polymers.
- Data Scientists and Analysts: Data professionals keen on applying their machine learning expertise to specialized domains like polymer informatics.
- Academic Researchers and Students: Faculty and students from universities and research institutions who are studying or are involved in polymer science, materials science, chemical engineering, or computational chemistry.
- R&D Professionals: Individuals working in research and development sectors of industries that produce or utilize polymeric materials, who are looking to innovate or improve products using data-driven approaches.
- Technology Developers: Developers and technologists who are building tools and platforms for material informatics and need to understand the application context and technical requirements in the polymer domain.
This course would be beneficial for anyone interested in the intersection of polymer science and machine learning, seeking to leverage the predictive power of data to solve complex problems in materials design and optimization. This workshop is designed not only to broaden your technical expertise but also to provide a strategic vantage point from which to view future developments in the field, making it an essential investment for anyone committed to excelling in plastic engineering and related disciplines.
Why Should You Attend?
Are you in the business of material development or compounding, where creating new materials demands considerable time, effort, and cost? AI has the potential to significantly reduce these challenges!
Are you currently developing new materials and feeling overwhelmed by the number of experiments required to achieve a final formulation?
Has your company accumulated a vast amount of data from developing materials and compounds, and are you looking to effectively leverage that information to convert it into knowledge and develop new materials?
Are you interested in how AI can optimize your current materials and formulations?
If the answer is YES to any of these questions, then this workshop is for you!
