Christoph Schran

Research

We are a group of computational scientists fascinated by the study of challenging materials by means of computational methods. We primarily focus on developing machine learning potentials in order to provide insight into complex aqueous systems, for which accurate and efficient representations of potential energy surfaces are urgently needed.

Examples of this are the exploration of the behaviour of water at interfaces, or confined in nanotubes and nanochannels, the mechanistic details of ion dissolution and proton transfer in water, as well as the interplay of CO2 and aqueous solutions.