We develop and apply machine learning-enabled simulation & theory tools to understand and predict the properties of polymers and soft materials for energy, sustainability, and technology applications. Our computational approaches help uncover the chemical and physical driving forces underlying thermodynamics, transport, and assembly in fluids and functional soft matter.

Current research interests include:

• Polymer sustainability, polymer degradation, polymer recycling & upcycling

• Polymer physics, solution processing of polymers, polymer architecture effects

• Polymer- and nanoparticle-based electrical & optical nanomaterials

• Liquid state theory, molecular simulations, and statistical mechanics

• Machine learning interaction potentials for fluids and materials