Research overview
Research Overview: Engineering Charged Soft Matter Across Scales
Soft matter is everywhere. It shapes living tissues, enables flexible devices, controls ion transport, and forms the foundation of many emerging energy technologies. Our lab focuses on a particularly rich and important class of soft materials: charged soft matter containing mobile ions, including polyelectrolytes, polymer electrolytes, biological soft tissues, and ion-conducting functional polymers.
These systems are scientifically fascinating because they are governed by a deep coupling between two long-range effects: polymer chain correlations and electrostatic interactions. Polymer chains are not simple particles. Their connectivity, conformations, and collective fluctuations create correlations across many length scales. At the same time, ionic species generate electrostatic interactions that are also long-ranged and highly sensitive to local structure, solvation, and external fields.
Our central goal is to understand how these two effects work together to determine material behavior. By connecting microscopic structures to macroscopic properties and device-level functions, we aim to build a rational framework for designing next-generation soft materials for energy storage, ion transport, separation, and flexible electronics.
Our Research Methods: Theory, Simulation, and AI for Soft Matter Design
We use a highly interdisciplinary and multiscale approach that combines molecular theory, computer simulation, and data-driven artificial intelligence.
At the theoretical level, we develop advanced polymer field theories to describe the collective behavior of charged polymers and ions. These methods allow us to capture how chain connectivity, electrostatics, and ion solvation jointly control phase behavior and material properties.
At the simulation level, we use molecular dynamics, coarse-grained models, and field-coupled simulations to study soft matter systems under realistic conditions. In particular, we develop simulation strategies for systems exposed to external electric fields, which are essential for understanding electrochemical interfaces and energy devices.
At the data-driven level, we integrate machine learning and AI methods, including structure-property models and equivariant graph neural networks, to accelerate material discovery. These tools help us identify hidden physical patterns, screen large chemical spaces, and design new polymers with targeted properties.
We also work closely with experimental collaborators. This connection ensures that our theoretical and computational models are not only physically meaningful, but also useful for real materials and real devices.
Research Direction I: Programming Assembly and Packing in Bulk Soft Materials
Charged polymers and block copolymers can self-assemble into remarkably diverse structures in bulk solutions and melts. These structures are controlled by a delicate balance among chain entropy, local interactions, electrostatics, and ion solvation.
A major focus of our lab is to understand how salt doping and selective ion solvation influence microphase separation. In polymer electrolytes and charged block copolymers, ions are not passive additives. They can reshape the effective interactions between polymer segments, change domain spacing, and stabilize new ordered morphologies.
Our theoretical work has revealed deep connections between electrostatics and polymer chain correlations. In some cases, these two effects can be expressed through mathematically equivalent frameworks. This insight allows us to design new routes for controlling self-assembly.
By manipulating charge distribution, ion solvation, and chain architecture, we aim to stabilize complex packing structures that go beyond conventional soft matter phases. These include highly ordered spherical phases, intricate networks, and exotic structures related to Frank-Kasper phases. Such structures may provide new opportunities for designing materials with advanced mechanical, transport, and optical properties.
Research Direction II: Controlling Structure Formation at Interfaces and Surfaces
Interfaces are where many important soft matter processes become functional. In electrochemical devices, the interface between an electrode and an electrolyte controls charge storage, ion transport, stability, and failure. Understanding and controlling interfacial structure formation is therefore essential for the design of advanced energy materials.
Our lab studies soft matter assembly at surfaces and interfaces, especially under external electric fields. We develop and use field-coupled molecular simulations to explore how polymers, ions, and charged surfaces interact dynamically.
One important application is the study of electric double-layer capacitors. We investigate how engineered polymer charge sequences affect adsorption, interfacial organization, and charging dynamics. By tuning molecular sequence and charge pattern, we seek to control how soft charged materials respond to electric fields.
Another major application is the design of soft polymer coatings for lithium metal batteries. Lithium dendrite growth remains a serious challenge for next-generation batteries. We simulate how polymer coatings interact with lithium surfaces and ionic environments, with the goal of identifying design principles for suppressing dendrite formation and improving battery safety.
Research Direction III: Designing Functional Polymers for Ion Transport
Ion transport is central to many modern technologies, including solid-state batteries, separation membranes, sensors, and flexible electronics. However, designing polymers with fast, selective, and stable ion transport remains challenging.
Our lab studies functional polymers in confined spaces and complex chemical environments. These include solid polymer electrolytes, porous organic cage membranes, polyimides, and other ion-conducting soft materials.
A key concept in our work is generalized ion solvation. Traditional models often treat ion transport mainly through diffusion or simple binding interactions. In real polymer systems, ion motion is controlled by a broader set of molecular factors, including local coordination, segmental motion, dielectric environment, free volume, and polymer architecture.
We use generalized ion solvation models to understand and navigate the trade-off between ion dissociation and ion diffusion. Strong ion-polymer interactions may help dissociate salts, but they can also slow down ion motion. Weak interactions may allow faster diffusion, but they may reduce the number of mobile charge carriers. Our goal is to identify molecular designs that balance these effects.
By combining physical insight with AI and machine learning, we rapidly screen candidate polymers and predict their transport properties. This strategy allows us to move from understanding to design, and ultimately toward functional materials with high ionic conductivity, strong electrochemical stability, and robust performance in practical devices.
Toward Rational Design of Future Soft Materials
Across these research directions, our lab seeks to answer one broad question:
How can we use molecular-level physics to design soft materials with predictable and programmable functions?
We believe the answer lies in combining classical principles of polymer physics with modern computational methods and AI. By respecting the deep physical foundations of soft matter while embracing new tools for discovery, we aim to build a bridge from fundamental theory to advanced materials and devices.
Our work is especially suited for students who are excited by physics, computation, materials science, and interdisciplinary research. Through theory, simulation, and data-driven design, we are working to uncover the hidden rules that govern charged soft matter—and to use those rules to create materials for the future.