Master´s Projects

Computational and theoretical studies of materials properties at quantum level using advanced
electronic structure methods, diagrammatic quantum Monte Carlo, spin Hamiltonians, and artificial intelligence. Current thesis topics: (i) polaron quasiparticles; (ii) entanglement between spin, orbit, lattice and electron correlation; (iii) computational surfaces  including reconstructions, polarity, topology and adsorption. The research can involve both application of existing packages or code development and are often conducted in collaboration with experimental groups. The Master student (m/f/d) will be integrated in a friendly research group and will actively participate in discussions, seminars and, if needed, short internships with foreign collaborators.

Ab-initio modeling of electronic and optical properties of semiconductors using density functional theory (DFT) and many-body perturbation theory, with particular focus on low-dimesional layered materials. Current research questions address band-gap engineering and tailoring the electronic and optical properties of semiconductors by low-dimensional structuring. These investigations are performed using existing computer codes. Research-oriented Master projects for students of the teacher's training program in Physics (MEd) can be offered as well.

Our group develops the Vienna ab initio simulation package (VASP). This code is used by more than 3000 research groups worldwide. Current research includes developments to solve the one-particle Schrödinger equation for solids, quantum Monte Carlo methods to solve the many-body Schrödinger equation, and machine learning methods to accelerate the computation of material properties. We also apply the developed methods to a variety of condensed matter problems of current interest, including energy materials and problems in surface sciences and catalysis. Master's student projects can cover any of the current research areas and typically involve some coding, for example using Python. However, most Master's projects are more applied and involve computations with existing computer codes, such as VASP.

The Maurer group develops and applies new computational simulation methods and prediction tools that fuse machine learning (ML) and data-driven approaches with quantum mechanical (QM) electronic structure and dynamical simulation methods. We are interested in application areas ranging from heterogeneous photo-/electrocatalysis, nanostructured and functionalized interfaces, to surface spectroscopy.

Examples of current project areas include:

•  Machine-learning-accelerated simulation of light-driven and ultrafast dynamics at surfaces in the context of photocatalysis and hyperthermal scattering
• Development of novel machine-learning surrogate models in electronic structure theory
• Machine-learning-driven design of functional two-dimensional materials and organic thin films

The methods we develop remove existing bottlenecks in simulation capabilities that limit the length and time scales and the complexity of simulations of structure, dynamics, and spectroscopy of materials. More importantly, hybrid ML/QM methods enable fundamentally new prediction approaches that defy the conventional structure-to-property paradigm that underpins materials science, enabling inverse property-driven design of novel materials.

All MSc projects include basic training and tutorial work to get students of all backgrounds accustomed to the underlying theory and methodology. This involves highly transferable skills training including usage of Linux operating systems, coding (Python, Julia, Fortran), the use of electronic structure software and high-performance compute clusters. Research in the group commonly involves close collaboration with experimental research groups.