March 24, 2021: “Machine Learning-accelerated Molecular Design of High-temperature Polymers: Shifting from Thomas Edison to Iron Man”

March 24, 2021
4:00 p.m.
Dr. Ying Li, University of Connecticut
For Zoom credentials, please email cloan@vt.edu
Faculty Host: Dr. Michael Philen

Abstract: Light-weight and high-strength polymers with outstanding high-temperature properties (glass transition temperature > 200°C) have been identified as promising materials for aerospace engineering and defense applications. Nevertheless, the design and development of high-temperature polymers have been an experimental-driven and trial-and-error process guided by experience, intuition, and conceptual insights. This Edisonian approach is often costly, slow, biased towards certain chemical space domains, and limited to relatively small-scale studies, which may easily miss promising compounds. A grand challenge in designing these organic materials is the vast design space on the order of 10100, defined by the almost infinite combinations of chemical elements, molecular structures, and synthesis conditions. To tackle this challenge, I will present our recent works on developing a data-driven molecular simulation strategy that can efficiently discover and design novel polymers with unprecedented yet predictable combinations of properties. Specifically, we use machine-learning techniques to build a meaningful chemistry-property relation for polymeric materials. Then, we utilize generative adversarial networks, combined with Reinforcement Learning models, for the inverse molecular design of high-temperature polymers. Eventually, we apply the experimentally validated molecular dynamics simulations to verify these molecular designs. We expect this work can address a wide range of scientific questions in computational materials design and synthesis-structure-property relationships for polymeric materials. It will also benefit the broader scientific community and industry, who are interested in developing new types of polymers for extreme environments, sustainable energy solutions, and biomedical applications.

Bio: Dr. Ying Li joined the University of Connecticut in 2015 as an Assistant Professor in the Department of Mechanical Engineering. He received his Ph.D. in 2015 from Northwestern University, focusing on the multiscale modeling of soft matter and related biomedical applications. His current research interests are: multiscale modeling, computational materials design, mechanics and physics of polymers, machine learning-accelerated polymer design. Dr. Li’s achievements in research have been widely recognized by fellowships and awards, including Air Force’s Young Investigator Award (2020), 3M Non-Tenured Faculty Award (2020), ASME Haythornthwaite Young Investigator Award (2019), NSF CRII Award (2018) and multiple best paper awards from major conferences. He has authored and co-authored about 100 peer-reviewed journal articles, including Physical Review Letters, ACS Nano, Biomaterials, Nanoscale, Macromolecules, Journal of Mechanics and Physics of Solids and Journal of Fluid Mechanics, etc. He has been invited as a reviewer for more than 70 international journals, such as Nature Communications and Science Advances. He currently serves as the Topic Editor of MDPI-Polymers, a leading international journal in the polymer field.