Institute of Solid State Physics
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Reinforcement Learning Strategies for Molecular Control on Different Substrates Using Scanning Probe Microscopy | |
Understanding the intricate interactions between individual molecules and mastering their manipulation are pivotal for constructing atomically precise nanostructures with novel material properties. While chemical methods from solution-based synthesis exploit intermolecular interactions to form structures, they do not offer the flexibility needed for arbitrary arrangements. Scanning probe microscopy (SPM) stands out as a unique tool capable of imaging and manipulating individual molecules and atoms. However, the interaction processes involved are complex and vary significantly across different molecules and substrates. This variability poses challenges for constructing even moderately sized nanostructures. | |
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
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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. | |
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.
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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. | |
