We design computational tools to accelerate the discovery and optimization of advanced materials and formulations, bridging fundamental molecular science with practical applications in pharmaceuticals, energy, and sustainability.

Our Research

We combine molecular dynamics, enhanced sampling, and rare-event methods with machine learning to probe structure, dynamics, and phase behavior in molecular and soft-matter systems. A central theme is predicting and controlling nucleation, crystallization, and amorphous structure to enable better-performing materials in areas such as drug products, functional polymers, and catalytic and interfacial systems.

Our group often works closely with experimental collaborators, using simulations to interpret measurements and guide the design of new materials, processing routes, and formulations. This integrated approach helps identify molecular-level design rules that can be transferred across different chemistries and application domains.

Areas of interest

• Molecular modeling and multiscale simulation of soft and condensed matter (molecular crystals, amorphous solids, polymers, biomaterials).
• Crystal nucleation, growth, and polymorphism in molecular and pharmaceutical systems.
• Co-amorphous and amorphous formulations, including polymer–drug and biomolecular materials for improved stability and performance.
• Interfacial phenomena, wetting, and confinement effects in porous, catalytic, and nanostructured materials.
• Data-driven collective variables, enhanced sampling, and machine learning for rare events and materials discovery.
• Environmentally relevant and energy-related systems, including separations, green surfactants, and reactive or redox-active materials.