AI-Accelerated Atomistic Modeling

We develop and apply machine-learning-enhanced atomistic simulation frameworks that extend first-principles calculations to larger length and time scales, enabling accurate and efficient modeling of complex materials systems.

Interface Chemistry under Realistic Conditions

We investigate surfaces, interfaces, and defects at the atomic scale under realistic thermodynamic and chemical environments, focusing on reconstruction, non-stoichiometry, and charge transfer phenomena relevant to functional materials.

Computational Catalysis and Energy Conversion Materials

We employ advanced electronic-structure calculations and atomistic simulations to uncover reaction mechanisms and structure-activity relationships in catalytic and energy-conversion materials, including heterogeneous and electrochemical systems.

Data-Driven Materials Discovery

We leverage data-driven modeling and artificial intelligence to explore complex materials spaces, accelerate materials discovery, and connect atomistic simulations with experimentally relevant material performance.