Our research focuses on developing computational and AI frameworks that connect first-principles descriptions of crystalline and molecular microstates with their macroscopic thermodynamic and kinetic properties. By bridging length scales, time scales, and experimental phenomena, these frameworks accelerate materials discovery while deepening our fundamental understanding of materials chemistry for technological development.

Currently, we are particularly interested in:

• Computational modeling of complex materials under realistic experimental conditions.
• Atomistic simulations that integrate statistical mechanics with first-principles calculations.
• Method development in AI for science: simulations for large and open systems, generative dynamics, and experimental alignment.
• Building scientific agents as discovery loops via physics-grounded simulations.