(2be) Multiscale Computational Design of Polymeric Materials for Sustainability and Healthcare

My research group will focus on predicting the structure-property relation of functional polymers with controlled monomer sequence and molecular architecture. Nature is a master at the rational design of functional polymers. Using limited types of amino acids as building blocks, Nature has designed proteins by controlling their sequence and arrangement. However, it takes Nature billions of years since it relies on the Edisonian approach, characterized by trial and error. The discovery and development of functional polymers can be more efficient and productive under the guidance of predictive models for the structure-property relation. This relation is a manifestation of polymer structure and dynamics covering wide spatial and temporal ranges and cannot be effectively investigated by a single-resolution model.

My research group will explore the vast design space of polymeric materials and build multiscale structure-property relations of new functional polymers by a combination of three approaches: (1) “top-down” coarse-grained modeling and theoretical polymer physics for chain structure-property relation; (2) “bottom-up” chemistry-specific multiscale simulation methods for mechanics and rheology; (3) machine learning algorithm for efficient exploration of chemical design space. With my experience of diverse and fruitful collaborations with experimental groups and chemical companies, the research of my group will bridge the gap between the synthesis and application of new functional polymers for healthcare and sustainability. In the following, I outline three research thrusts:

1. Sequence-Controlled Polyampholyte Coacervates for Biomedical Applications: Membraneless organelles (MLOs) are condensed phases formed in cells through liquid-liquid phase separation of intrinsically disordered proteins (IDPs). They organize and regulate biochemical reactions in cells by providing specific microenvironments and sequestering specific biomolecules. The simplest model capable of capturing the phase behavior and molecule sequestration properties of MLOs is the polyampholyte coacervate. Using a combination of coarse-grained simulations and theoretical polymer physics, we will study the effect of charge sequence on the formation of multiphase polyampholyte coacervates and their selective sequestration of proteins and nucleic acids. This will help understand the mechanism of the formation of MLOs and guide the rational design of polymers for protein encapsulation and drug delivery.

2. Computational Design of Sustainable Polymers: A sustainable future of polymeric materials relies on their recyclability. However, it is challenging to design polymers with mechanical and rheological properties suitable for recycling processes, since they are sensitive to the molecular structure and involve molecular relaxations occurring over a wide range of time and length scales. My group will develop a multiscale computational method to model recyclable polymers with various chemical structures of repeat units and different chain architectures, which will be incorporated with machine learning algorithms for the reverse design of target properties.

3. Mechanochemistry in Stimulus-Responsive Polymers: Transduction between mechanical and chemical energy can be harnessed to program responsive materials, generate and store energy, and drive biological activities. My group will study the mechanochemistry in polymeric materials to understand the energy transduction pathway. For this purpose, we will develop a multiscale simulation framework, accelerated by machine learning algorithms, to efficiently capture the structural and conformational evolution of polymer chains undergoing chemical reactions and mechanical deformations.

Teaching Interests

We are living in an age of information explosion and rapid technological revolution, calling for critical thinkers and lifelong learners who can stay relevant and acquire new skills throughout their careers. As a teacher and mentor, I believe that the ultimate goal of education is to foster critical thinking and life-long learning skills. To achieve this goal, I am committed to providing students with an engaging and inclusive learning environment in both classroom and lab that encourages experimentation and exploration.

For example, I am interested in teaching thermodynamics and statistical mechanics, where I engage students by creating an interactive learning environment in courses that requires rigorous understanding of physical laws. This often involves breaking down a complicated concept or derivation into simple pieces in front of students and demonstrating this process step by step on the whiteboard/chalkboard so students can closely follow.

I will also focus on teaching problem solving skills and expose students to the latest developments in chemical engineering. For example, I will incorporate python programming and machine learning in courses such as “numerical methods for chemical engineering”, to students can be prepared for the rapidly changing world.

Background

I received my Ph.D. in Polymer Science in 2019 from the University of Akron, under the supervision of Prof. Andrey Dobrynin. My graduate work was focused on the rational design of polymeric materials through molecular architectures. Using a combination of coarse-grained molecular dynamics simulations and theoretical polymer physics, we reverse-engineered the softness and nonlinear elasticity of biological tissues by comb and bottlebrush networks with controlled molecular architectures. Since 2019 Fall, I have been working as a postdoc with Prof. Juan de Pablo at the University of Chicago. My postdoctoral works are devoted to understanding the structure and dynamics of polyelectrolyte coacervates, developing multiscale modeling approaches for polymer rheology, and combining high throughput simulations with machine learning algorithms for material discovery. Most of my research activities are carried out in close collaboration with experimentalists from universities and companies.