Research

Journey through our research group activities

We develop advanced quantum-mechanical and machine learning methods and apply them to achieve increasingly reliable description of a wide range of realistic materials, such as biomolecules, nanostructures, disordered solids, and organic/inorganic interfaces.

Research objectives

Overview

Development of highly accurate and efficient methods for the inclusion of dispersion interactions in first-principles calculations of the properties of molecules and materials.
Development of machine learning force fields for the accurate and cost-efficient prediction of energies, forces, and structures of molecules and materials, enabling even the study of larger and more complex systems that cannot be assessed by conventional first-principles molecular dynamics methods.
Methodologies for the accurate and reliable crystal structure prediction of molecular crystal polymorphs.

Computer-aided molecular design with broad implications for drug discovery and materials science.
Quantum field theory for (inter-/intra-) molecular interactions.
Development of quantum methods for large systems and applications to study complex phenomena in physics, chemistry and biology.
Application of quantum chemistry methods to cosmological problems.