Studying the impact of dynamic disorder on charge transport in MOFs combining machine-learned force fields with the calculation of electronic couplings using DFT
Charge transport in organic semiconductors is crucially impacted by the relative arrangement of neighboring molecules, as it determines the electronic coupling between the molecules and, thus, carrier hopping rates and crystal electron effective masses. As we have shown recently, there is a natural tendency in organic semiconductors to minimize the said electronic coupling, as this also reduces the exchange repulsion between the molecules. Here, metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) offer the possibility to use their 3D network structure for positioning semiconducting molecules such that this problem can be overcome. Consequently, by a careful framework design, the semiconducting building blocks can be arranged in a strong-coupling configuration. This is comparably straightforward to show for the static case. At room temperature, the thermally triggered displacement amplitudes of the involved building blocks can, however, become relatively large. This calls for an improved description of the situation including dynamic disorder. During the present thesis, for systematically varied network structures this will be studied by analyzing the time-dependent couplings along molecular dynamics trajectories calculated using highly accurate machine-learned force fields.
Machine-Learning Physical Equations from kMC Simulations
Machine-Learning Physical Equations from kMC Simulations
Contact: Oliver Hofmann (email@example.com)
Computational Prediction of Optimal Growth Conditions of Organic Thin Films
Organic thin films play an important role in many applications, ranging from OLED-TVs and mobile phone displays to mechanically flexible photovoltaic cells. A particular challenge for these materials, however, is that they can assume many different structures (polymorphs), with substantially different properties. Which polymorph forms in a particular experiment is strongly dependent on the processing conditions, which are often optimized in a long, tedious and incredibly boring process by trial and error.
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.
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.
Preparation of a photonic single crystal
The first part of the work is the preparation of a photonic single crystal by 3D printing technique. 3-dimensional stacking of piles will provide a periodic lattice with periodicity in the range of 1 µm, a characteristic length comparable with the wavelength of visible light. In a subsequent step en experimental set-up have to be built to perform diffraction experiments using visible light. Geometries of classical X-ray diffraction techniques will be used using white light (Laue diffraction) or monochromatic light (single crystal diffraction by using a four circle goniometer).
The molecule HATCN is a strong electron acceptor that is commercially used in OLEDs to modify the property of metal substrates. Adsorbed on silver, this molecule shows unusual, fascinating physics. At low coverage, the molecule forms honeycomb patterns, which can be exploited as epitaxial growth template. When the coverage is increased, however, the first monolayer rearranges. This drastically changes the material properties, in particular the system’s work function.