(4h) Machine-Aided Design and Manufacturing of Polymeric Materials

Over the past century, substantial efforts had been made to understand the intriguing behaviors of polymeric materials. However, with great diversity in their molecular architecture, intra- and inter-structural dynamics, it is still rather challenging to predict the properties of these materials under different physicochemical conditions. Recent applications of artificial intelligence on small-molecule chemistry seems to present a promising solution to the above challenges but applying these advances to polymer is difficult given the lack of well-defined molecular structures as well as the scarcity of processing and characterization data. There is thus a great interest in machine-aided polymer engineering, where new hybrid computational approaches complemented with multiscale characterization methods are required to bridge the gaps among the synthetic chemistry, processing conditions, molecular structure, and macroscopic properties and behaviors.

With the above perspectives, my research will focus on unifying first-principle theories as well as experimental characterization with machine-learning in polymer science, to enable fast property prediction and smart processing for polymers and their associated soft materials with a variety of mechanical performance. Such methodology would then be further applied to the recycling of multi-stream plastic waste, through which a copolymer of desired mechanical properties will be manufactured. Here I elaborate three future research topics, in which first-principle theories with machine learning methods will be combined to enable predictive polymeric materials engineering. In Topic 1, I will build the methodology for predicting mechanical properties of polymers from its oligomers. In Topic 2, I will turn to the machine-aided processing of polymeric materials under structural transitions. In Topic 3, as a practical application of the approaches developed in Topics 1 and 2, novel methods of recycling of plastic wastes by manufacturing copolymers with versatile mechanical properties will be proposed.

Research Experience

As a good starting point on my proposed research topics, an enhanced understanding of the link between macroscopic properties of polymers and their coarse-grained molecular structure can readily be gained from my previous achievements (with my PhD advisor Prof. Ronald Larson, University of Michigan) on modeling the rheology of living polymer solutions and polymeric glasses. Among those works, multiscale models and simulation tools were built, which for the first time, allows not only quantitative prediction on the rheology of those materials but also estimation of important structural and configurational change under varying conditions. With respect to synthetic chemistry and the associated processing variables, the state-of-the-art knowledge was achieved (with my Post-doc advisor Prof. Bradley Olsen, MIT) on the kinetics of complex reaction network for radical mediated functionalization of polyolefins. Combining quantum theory, model compound studies, polymer physics as well as modeling and simulation techniques, a topology-based first-principle model was developed and demonstrated its good predictive power on estimating both the yield of functional content and the evolution of linear viscoelasticity during the melt processing. A further exploit of this success leads to my ongoing progress on establishing a realistic synthetic pathway to recycle wasted polyolefins into high performance elastomer. Eventually, with chemical identity of polymers being represented by BigSMILES line notation (currently extended to include non-covalent chemistry), a simulation data schema under the framework of our recently developed data platform CRIPT (Community Resource for Innovation in Polymer Technology) was also proposed (with my Post-doc coadvisor, Dr. Debra Audus, NIST) to unify both experimental and computational data of polymeric materials for possible machine learning work in the future.

In summary, my broad experiences with the dynamics of soft materials (i.e., micelles, glasses, and molten polymers) as well as with their modeling and experimental characterizations (i.e., computational and analytical chemistry, rheology), will help me to make good progress on the proposed interdisciplinary research topics regarding the theme of “Machine-aided Design and Manufacturing of Polymeric Materials.”

Teaching Interests

Building on my research experiences in both theory and experiments, I am interested in developing course settings that benefit students from diverse backgrounds with versatile format of learning materials to facilitate their course and lab based studies. With the aim of enhancing students’ participation and to provide me with real-time feedback, the classes will be conducted with a mixture of critical reading, learning of new concepts, and open-ended discussions among student teams. Depending on the level of the course, I will balance the weight of content among fundamental knowledge, team-based in-class discussion, out-of-class self-education, as well as real-world projects for cultivation of creativity. Throughout the course, I will assemble and lead my teaching team to assist students to overcome both academic and social barriers by creating an inclusive, respectful, and fair learning environment.

Among the commonly taught courses in chemical engineering and material science, I am capable and excited to teach Fluid Mechanics, Polymer Physics and Chemistry, Applied Math and Numerical Methods, Mass and Heat Transfer, Chemical Reaction and Kinetics, Thermodynamics, and Statistical Mechanics. In addition, I am also enthusiastic about developing new courses related to my research areas, which include polymer recycling and manufacturing, mathematic modeling and simulation, polymer representation and encoding as well as machine-learning and artificial intelligence. Such topics might be arranged as electives under the category of advanced polymer science or soft matter physics. With the increasing demand world-wide for sustainable materials and information technologies, providing courses on the relevant subjects are critical in supporting students in their pursuit of rewarding careers.

Teaching Experience

Given my previous teaching experience at the University of Michigan, I have the ability and desire to become an excellent teacher under both conventional classroom and lab setting. My abilities to convey complex concepts in simple languages to students as well as facilitate constructive discussions in classroom have been well recognized in those teaching endeavors. Comments from my formal teaching evaluations at the end of the classes include “The instructor is approachable, knowledgeable and has established a good rapport with his students. He is very organized and helpful, and his students trust his judgement.”

In conclusion, having been a student for many years in multiple settings has made me fully aware of the critical influence of teaching and mentoring, and of appreciation and respect for different backgrounds, on the development of young minds. Thus, I deeply value the opportunity to assist the development of students of diverse personality types, ethnic and cultural backgrounds, and levels of previous accomplishments, towards attainment of fulfilling careers and lives.

Selected Achievements

• 2020 A Community Resource for Innovation in Polymer Technology (CRIPT): http://criptapp.herokuapp.com/index/ (as one of the founders)
• 2016 – 2017 Rackham Predoctoral Fellowship
• 2016 North American Connect & Development Award (as external partner of P&G)
• 2016 Zou, W.; Lu, J.; et al. Chapter 18 — Petroleum and Natural Gas Transportation and Storage. Exploration and Production of Petroleum and Natural Gas, ASTM International Publication, West Conshohocken, PA. DOI: 10.1520/MNL7320140023.

Funded Proposals

• Upcycling rubber and polyolefin waste into performance elastomers. Proposal to IWC Schaffhausen.
• Improving the sustainability of Fortrex resin. Proposal to Cooper-Standard Inc.