Thermal transport is of distinct relevance for most technological applications of materials. This is particularly true for organic semiconductors (OSCs) and metal-organic frameworks (MOFs). In the course of the advertised theses (one for OSCs and one for MOFs) we will use our recently developed strategy for calculating thermal transport properties of complex materials employing non-equilibrium molecular dynamics (NEMD) with highly accurate, system-sepcifically parametrized force fields. The said force fields will be obtained combining on-the-fly machine learning approaches based on periodic DFT codes with highly efficient moment-tensor potentials. The NEMD simulations analyze thermal transport in real space. As an alternative approach, the machine-learned potentials will also be used for accurately calculating phonon properties, which - when combined with the Boltzmann Transport Equation - allow analyzing thermal transport in reciprocal space.
COMPENSATION: 440€ per month / for at least 6 months
CONTACT: Egbert Zojer (Egbert.Zojer@tugraz.at)