Machine Learning-Accelerated Molecular Design of Innovative Polymers: Advanced Manufacturing, Extreme Conditions, and Sustainable Energy Solutions
MIE Faculty Candidate Seminar
April 25, 2022
2:00 PM - 3:00 PM
1043 ERF and on Zoom at https://uic.zoom.us/j/81717822512?pwd=TG9qM2x1NGVHaVR2Q1g5QW5wR01OQT09
842 W. Taylor St., Chicago, IL 60607
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Presenter: Ying Li, PhD, University of Connecticut
Location: 1043 ERF or on Zoom.
Meeting ID: 817 1782 2512
Abstract: Polymeric materials are key enablers in aerospace, mechanical, civil, and environmental engineering, such as reverse osmosis membranes for water treatment and desalination, coatings for building skins and antifouling materials, etc. Nevertheless, the design and development of innovative 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 polymeric 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 innovative 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, which are interested in developing new types of polymers for advanced manufacturing, extreme conditions, and sustainable energy solutions.
Speaker 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 research interests are: multiscale modeling, computational materials design, mechanics and physics of polymers, machine learning-accelerated polymer design. Li’s achievements in research have been widely recognized by fellowships and awards, including 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 authored and co-authored more than 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 90 international journals, such as Nature Communications and Science Advances. Li’s lab is 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.
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