Computational and Theoretical Chemistry
In our research group, we develop cutting-edge computational tools to understand complex phenomena in chemistry and physics. Our work combines statistical physics, quantum mechanics, and machine learning, and focuses on four main directions:
- Simulation of Slow Chemical Processes – Developing advanced algorithms for efficient sampling of slow chemical dynamics, such as conformational ensembles of biomolecules and prediction of polymorphism in molecular crystals.
- Quantum Dynamics of Bosons and Fermions – Designing path-integral-based methods for simulating strongly correlated quantum systems and efficient algorithms for capturing their dynamical properties.
- Statistical Physics of Artificial Intelligence – Investigating the learning process of neural networks using tools from statistical mechanics.
We welcome curious students who enjoy thinking, coding, and interdisciplinary science.
