Research

Our group develops and applies state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD), machine-learning interatomic potentials, and kinetic modeling. These tools reveal thermodynamic, kinetic, and transport phenomena that are difficult to resolve experimentally, while enabling systematic exploration of materials, compositions, interfaces, and reaction pathways. By connecting electronic structure and atomistic dynamics with experimentally measurable properties, we accelerate materials discovery and provide mechanistic guidance for synthesis and process optimization.

• Integrating DFT and microkinetic modeling to uncover reaction mechanisms, identify active sites, and guide catalyst design

• Predicting thermodynamic and kinetic pathways for synthesizing functional materials, including perovskites, hexagonal boron nitride, and icosahedral boron-rich compounds

• Developing data-efficient machine-learning interatomic potentials for large-scale simulations of catalytic interfaces, liquids, and chemically complex materials

• Modeling defect chemistry and coupled proton, ion, and electron transport in solid-oxide electrochemical systems

Past research projects