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 MSc project areas include:
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Machine-learning-accelerated simulation of light-driven and ultrafast dynamics at surfaces in the context of photocatalysis and hyperthermal scattering
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Development of novel machine-learning surrogate models in electronic structure theory
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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.
Contact for Questions: Prof. Reinhard Maurer Email: reinhard.maurer@univie.ac.at