Institute of Solid State Physics


Master Projects

The electrostatic design of materials

Collective electrostatic effects arise, when when materials or interfaces comprise ordered assemblies of dipoles. These can, for example, be used to change the resistance carrier injection at interfaces by orders of magnitude. Recently, we have also shown that they can significantly impact the potential within poroes of metal-organic or covalent-organic frameworks, which is expected to directly impact redox processes (e.g., in batteries), catalytic processes, or excited state charge separation. The goal of this thesis is to explore to what extent such efects can be impacted by the presence of solvent molecules emplyong DFT-based band-structure calculations in combinations with molecular dynamics calculations using system-specifically amchine-learned force fields. COMPENSATION: 440€ per month / for at least 6 months

CONTACT: Egbert Zojer (Egbert.Zojer@tugraz.at)

Interpretation of µCT Data using KI      >> mehr >>

The interpretation of µCT measurements depends strongly on the quality of the segmentation of the data. In order to improve the segmentation of measured data we want to further explore KI methods in data segmentation. With these segmentations further quantitative interpretation of the µCT data will be done, in order to get a deeper insight into different materials.

Understanding thermal transport in OSCs and MOFs using highly accurate machine-learned force-fields

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)

Proof of surface crystallisation for anthraquinone

The preparation of anthraquinone thin films is performed by solution processing. The first step is the preparation of a dissolution of the molecule anthraquinone with a solvent (like tetrahydrofuran, toluene, chloroform, a.o.) and the second step is the deposition of the dissolution at a substrate surface by drop casting, dip-coating or spin coating. After evaporation of the solvents crystals of anthraquinone remain at the substrates surface. Two different mechanisms are assumed for the crystallization process at the substrates surface. On one hand the nucleation of crystals happens in the solution, crystals grow in the solution due to supersaturation and finally crystals remain at the substrate surface after the evaporation of the substrate. On the other hand, nucleation of the crystals happens at the substrate surface and the crystal nuclei grow und the expense of the molecules in the solution. The thesis should separate these two effects of surface crystallization on basis of anthraquinone thin films.

COMPENSATION: 440€ per month / 6 months
CONTACT: Roland Resel (roland.resel@tugraz.at)

Physics-Informed Deep Learning for Reactive Gas Transport in Paper

Simulating the transport of gasses such as organic volatile molecules through porous media is challenging when the complex geometry of real systems has to be considered, especially when transport is accompanied by sorption and/or chemical reactions. This complexity in geometry challenges traditional numerical solvers in their ability to capture the intricate details of the system in an accurate yet feasible manner.

The transport through paper is influenced by a combination of material properties, including the polarity of the organic compound, its diffusivity in the porous and solid phases of the paper, and the paper’s micro-structural features such as porosity and tortuosity. Physics-informed neural networks (PINNs) can successfully predict such material parameters and the subsequent transport through paper (DOI: 10.1007/s11242-022-01864-7).

Based on experimental migration data, this PINN approach shall be extended to two- and three-dimensional systems by incorporating microstructural information obtained via micro-CT measurements of paper into the framework of PINNs domain.

Contact: Karin Zojer

 

 


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