COMPUTATIONAL CHEMISTRY & MATERIALS MODELING
Our lab focuses on computational chemistry and materials simulation, combining theoretical calculations, materials design, and data analysis to investigate the properties and reaction mechanisms of energy materials, catalytic systems, and novel functional materials. Through atomistic simulations, we seek to understand how materials behave at the atomic and molecular scale—and to guide the rational design of more efficient functional materials.
High-entropy materials composed of multiple principal elements exhibit complex yet tunable structures and properties. We use computational simulations to study atomic arrangements, surface structures, and reaction behavior in HEAs, elucidating how elemental combinations affect stability and catalytic performance.
We focus on key reactions in sustainable energy conversion, investigating catalyst surface roles and identifying active sites that enhance efficiency:
We study nanoparticles, bimetallic nanoclusters, and core–shell structures to probe catalytic selectivity and activity, seeking to understand how surface geometry alters reaction pathways and product distributions.
Using density functional theory (DFT) at the electronic-structure level to address: why a material is more stable, why a specific site is more reactive, and how elemental doping reshapes the electronic structure.
Integrating machine learning with chemistry and materials science to accelerate screening and property prediction, identifying promising candidates from vast compositional spaces.
Open to collaborations in computational electrochemistry, DFT-based catalyst design, and theory–experiment joint projects.