At the beginning of the current century we are facing massive challenges due to the increasing global demand for energy, focused on two major issues. On one hand, conventional fossil fuel resources such as oil, natural gas and coal are limited and dwindling. On the other hand, the emissions due to combustion of fossil fuels evidently impact the chemical composition of our atmosphere, leading to adverse effects on the climate and environment. One potential path to tackle these challenges are provided by thermoelectric materials, which are required to drive thermoelectric generators that allow for a reliable, clean, emission-free conversion of (waste) heat into electricity. Until the mid 1990s, thermoelectrics had been considered inefficient and not economically relevant, but with enhanced structural engineering and intense research on novel complex materials the interest for thermoelectric materials has been recently revived. The efficiency of a thermoelectric material is governed by the so-called figure of merit zT, which is maximized by increasing the thermopower and electrical conductivity while reducing the thermal conductivity. These materials properties are however strongly interrelated, e.g. in most materials the thermal and electrical conductivities correlate with each other through the Wiedemann-Franz-law. Hence, the search for a material with a maximal zT poses a non-trivial materials design challenge.

An accurate description of the bulk lattice thermal transport, which is governed by phonon-phonon interactions, demands advanced simulation techniques and large HPC infrastructures. Solving the Boltzmann phonon transport equation requires the knowledge of the anharmonic energy contributions which give rise to phonon scattering, posing one of the computationally most demanding aspects in modeling thermal resistivity. Density functional perturbation theory and finite difference methods are currently state-of-the-art approaches, but remain computationally highly demanding. Furthermore, the interaction of phonons with electrons become increasingly important at elevated temperatures and have recently been the focus of research in thermoelectric materials. Finally, methods for modelling of transport properties on a large scale are required for the discovery of new materials with improved thermoelectric properties. My research focuses on novel approaches, amongst others based on methods from machine learning, signal processing and high-throughput techniques, in modeling transport properties to advance in silico discovery of thermoelectric materials.