Computational Design & Discovery of Soft Materials
In the Liang lab, we use a combination of theoretical calculations, computer simulations, and machine-learning accelerated multiscale modeling to design next-generation soft materials with controlled monomer sequences and tailored molecular architectures, targeting biomedical applications and sustainability.
Sequence-Controlled Polyelectrolytes for Biomedical Applications
Polyelectrolytes are charged polymers, including proteins, nucleic acids, and polysaccharides, that serve as fundamental building blocks of life. They can self-assemble into organized structures that regulate cellular processes. Condensation of polyelectrolytes is driven by sequence-dependent charge correlations and is thought to contribute to the formation of membraneless organelles, which spatially organize many biochemical reactions in cells. In the Liang lab, we investigate how charge sequences influence the polyelectrolyte condensation and the selective sequestration of proteins and nucleic acids, providing principles for the rational design of protocells and micelles for protein encapsulation and gene delivery.
Next Generation Sustainable Polymers by Computational Design
A sustainable future of polymeric materials relies on their circularity. However, it is challenging to design polymers with chemical and rheological properties suitable for recycling processes, as these properties are sensitive to the molecular structure and involve molecular motions occurring over a wide range of time and length scales. In the Liang lab, we develop computational methods to model sustainable polymers with diverse repeat unit chemistries and chain architectures. These models can be integrated with machine learning algorithms to enable the inverse design of polymers with target chemical and rheological properties.
Machine Learning Assisted Lightning Bottom-Up (MALIBU)
Multiscale Modeling Approach For Soft Materials
Soft materials, including polymers, colloids, and complex fluids, exhibit rich and tunable physical behaviors that emerge from interactions across multiple length and time scales. Their structure and dynamics govern important properties such as viscosity, elasticity, and self-assembly, which are essential for applications ranging from functional polymers to advanced composites. In the Liang lab, we develop multiscale modeling frameworks to predict and understand soft material behavior from atomistic to mesoscale levels, with a particular focus on entangled polymers and colloidal suspensions.