(2eh) Computational Design of Functional Polymer Materials

Nature is a master at rational design of functional polymers. Using limited amino acids and monosaccharides as building blocks, Nature has designed protein- and glycoprotein-based materials by controlling the sequence of building blocks and the architecture of polymers. Compared to Nature, our ability to design polymers is much inferior. The development of new functional polymers will benefit from predictive models for the structure-property relation of polymers. However, this relation is a manifestation of polymer structure and dynamics covering wide spatial and temporal ranges, and cannot be included in a single resolution model. My research focuses on predicting the multiscale properties of functional polymers with controlled monomer sequence and molecular architecture, using a combination of three approaches: (1) applying coarse-grained modeling and theoretical polymer physics to understand the structure-property relation of functional polymers; (2) developing multiscale simulation models to predict the mechanical and rheological properties of polymeric materials; (3) using machine learning algorithm to accelerate the multiscale modeling and to facilitate the exploration of the chemical space of polymers.

My research group will focus on two types of polymers due to their significant importance in healthcare and sustainability: ion-containing polymers (i.e., polyelectrolytes and polyampholytes) and commercial polymers characterized by polydispersity and complicated molecular architecture (such as low-density polyethylene and other polyolefins). The initial research efforts of my group will comprise three topics:

1. Sequence-Controlled Polyampholyte Coacervates as Models of (Artificial) Membraneless Organelles

It has been recently discovered that the liquid-liquid phase separation of charged biomacromolecules drives the formation of membraneless organelles in living cells, which are important micro/nanoscale bioreactors that selectively uptake nucleic acids and globular proteins for biochemical reactions. I will use coarse-grained modeling and theoretical polymer physics (such as scaling theory and the random phase approximation) to understand the effect of charge correlation on the formation of single/multi-phase membraneless organelles and on the selective sequester of biomacromolecules. This will help rational design of synthetic polyampholytes for therapeutic proteins and nucleotides delivery.

2. Multiscale Models for Recycling of Commercial Polymers

Recycling commercial polymers usually require reprocessing them in a molten state. In the molten state, polymers can be remolded into new products, decomposed into smaller molecules, or chemically functionalized into more valuable materials. To better understand and design the recycling procedure, I will first develop a multiscale modeling approach that predicts the rheology and transport of polymer chains with high polydispersity and complicated branching architecture. Then I will couple the multiscale model to chemical reactions that break or chemically modify polymer chains, so it can predict the product at the end of the recycling process.

3. Machine Learning Accelerated Multiscale Simulation of Polymer Rheology

The dynamics of long polymer chains can expand more than 12 orders of magnitude in time, from picoseconds for monomer relaxation to seconds for chain disentanglement. With the recent development of multiscale modeling, it is possible to cover such a wide temporal range by a combination of all-atom, coarse-grained, and slip-spring models. However, the performance of this multiscale model is still limited since the slowest disentanglement dynamics can only be simulated in a step-by-step manner from the slip-spring model. I will develop a machine learning accelerated slip-spring model, which is capable of updating the entanglements with arbitrary time steps. This will significantly speed up the simulation of entangled polymers and enable exploring the effect of polydispersity and chain architecture on rheology.

Teaching Interests

During my undergraduate and graduate school study, I was taught and mentored by excellent senior graduate students, teachers, and supervisors, from whom I learned the importance of self-motivation and practice in acquiring knowledge. I believe that the responsibility of a teacher/mentor is to motivate students by the clarity and beauty of science, and provide them with opportunities to practice in real-life examples. My teaching interests lie in the area of polymer physics, thermodynamics, statistical mechanics as well as molecular modeling. At the University of Akron, I served as a teaching assistant for an undergraduate course Introduction to Polymer Science. I also successfully mentored one high school student and one undergraduate student at the University of Akron and graduate students at the University of Chicago.

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.

Successful Proposals

Argonne Laboratory Computing Resource Center, UChicago Research Computing Cluster

Selected Publications

(1) Liang, H.; Yoshimoto, K.; Gil, P.; Kitabata, M.; Yamamoto, U.; de Pablo, J. J. Bottom-Up Multiscale Approach to Estimate Viscoelastic Properties of Entangled Polymer Melts with High Glass Transition Temperature. Macromolecules 2022, 55 (8), 3159–3165.

(2) Liang, H.; de Pablo, J. J. A Coarse-Grained Molecular Dynamics Study of Strongly Charged Polyelectrolyte Coacervates: Interfacial, Structural, and Dynamical Properties. Macromolecules 2022, 55 (10), 4146–4158.

(3) Liang, H.; Wang, Z.; Dobrynin, A. V. Scattering from Melts of Combs and Bottlebrushes: Molecular Dynamics Simulations and Theoretical Study. Macromolecules 2019, 52 (15), 5555–5562.

(4) Vatankhah-Varnosfaderani, M.; Keith, A. N.*; Cong, Y.*; Liang, H.*; Rosenthal, M.; Sztucki, M.; Clair, C.; Magonov, S.; Ivanov, D. A.; Dobrynin, A. V.; Sheiko, S. S. Chameleon-like Elastomers with Molecularly Encoded Strain-Adaptive Stiffening and Coloration. Science 2018, 359 (6383), 1509–1513.

(5) Liang, H.; Sheiko, S. S.; Dobrynin, A. V. Supersoft and Hyperelastic Polymer Networks with Brushlike Strands. Macromolecules 2018, 51 (2), 638–645.

(6) Liang, H.; Cao, Z.; Wang, Z.; Sheiko, S. S.; Dobrynin, A. V. Combs and Bottlebrushes in a Melt. Macromolecules 2017, 50 (8), 3430–3437.

(total: 32 publications, 14 first/co-first author, 2 additional first-author papers in preparation)