With solvent effects being central to several scientific disciplines like biology or (electro-)chemistry, computationally affordable methods are crucial to accurately simulate solvated systems. Implicit solvation models are widely used due to their simplicity and practical applicability in these kind of many-body problems. Here only the solute is explicitly modeled while the interaction with the solvent is coarse-grained into a continuum dielectric response, instead of considering individual solvent molecules. However, simply using the solvent medium’s experimental bulk dielectric constant neglects the fact that the behaviour of the solvent differs, sometimes drastically, in the vicinity of the solute compared to the bulk. Existing corrections to account for these entropic effects fail for charged systems and lack scientific foundation. These deficits of correctly accounting for multiple conformations in solution lead to great difficulties when calculating dissociation
constants (pKa values) or modeling other electrochemical processes using implicit solvation models.

The goal of this M.Sc. project is to introduce a better description that accounts for the orientational behaviour of solvent modecules, based on findings from previous extensive Molecular Dynamics simulations. The student will learn how to perform DFT calculations using implicit solvation and, in a second step, will compute the interaction of the interfacial water structure with the electric field obtained from these simulations. Basic knowledge of UNIX based operating systems and some experience with programming (and/or scripting) languages (preferably Python) is desirable, but not mandatory.
1

This Thesis is based on Deep Neural Networks (DNN) applied to generate new organic semiconductor (OS) molecules. Deep learning models project input data through several layers of nonlinearity and learn different levels of abstraction generating a link between the inputs and specific target outputs (or labels). Recently, there has been a fundamental work about using DNN algorithms for synthesising in-silico new OS [1] grounded on the seminal work by Gomez-Bombarelli et al. [2] and Kusner et al. [3] on the Grammar Variational AutoEncoders (GVAE) method. The thesis is focused on implementing a GVAE and further developing the method to include bigger molecules. The DNN will be applied to some realistic OS in order to evaluate the efficiency and accuracy in generating new molecules.

[1] P. B. Jørgensen et al., J. Chem. Phys., 148, 241735 (2018)

[2] R. Gómez-Bombarelli et al., Nat. Mat., 15, 1120-1128 (2016)

[3] M. J. Kusner, B. Paige, J. M. Hernández-Lobato, Grammar Variational Autoencoder, arXiv:1703.01925v1 [stat.ML] (2017)

PERC (Proton Electron Radiation Channel) is a new neutron decay facility that is currently being constructed at the FRM II research reactor. PERC is a versatile source of decay electrons and protons and allows the measurement of several correlation coefficients of the neutron decay with an order of magnitude improvement in precision over current experiments. Measurements of these coefficients are a sensitive probe to investigate the mechanisms of the weak interaction.

The core component of PERC is an 8 m long superconducting magnet. Electrons and protons produced by neutrons decaying inside the magnet are guided along the magnetic field lines to the main detector system behind the spectrometer. Our group has previously used scintillator-based electron detectors for precision measurements of neutron decay correlations. Building on this experience, we plan to use similar devices to detect backscattering in PERC. The purpose of these backscattering detectors is to identify events where the decay electron only deposits a fraction of its energy in the main detector.

The objective of this thesis is the design and demonstration of the backscattering detectors for PERC, including the development of a new read-out system for electron spectroscopy with plastic scintillators and silicon photomultipliers (SiPMs).

Tasks

• Familiarize yourself with neutron decay studies and particle-tracking simulations.

• Optimize the design of the backscattering detectors for PERC based on simulation data.

• Develop and test a SiPM-based read-out system for plastic scintillators suitable for precision electron spectroscopy.

• Construct and test a prototype of the backscattering detectors.

What we offer

We offer you an interesting opportunity to participate in the development of a new high-profile experiment, allowing you to gain diverse experiences in the fields of detector physics, electronics, and low-energy particle physics!

Für die Entwicklung von Theraphieansätzen ist es notwendig, die Wirkung von Medikamenten auf Organe zu untersuchen. Zu diesem Ziel werden inzwischen Organoide verwendet - das sind Organ ähnliche Zellverbände, die in einer 3d Zellkultur wachsen. Trotz der immensen Erfolge, sind ganz zentrale Fragen zu der Entwicklung dieser Strukturen weitestgehend ungeklärt. Im Rahmen der Arbeit sollen die physikalsichen Eigenschaften der Umgebung auf das Organoid-Wachstum untersucht werden. Es werden Mikrorehologische und Mikroskopsiche Methoden entwickelt und verwendet um die Abhängigkeit von Mikro-Umgebung auf Organoidentwicklung zu quantifizieren. 

Für das kommende JUNO (Jiangmen Underground Neutrino Observatory) Neutrinoexperiment werden in Garching möglichst empfindliche Radonmonitore für gasförmige Luft und N2 konzipiert und entwickelt. Im Rahmen dieser MSc-Arbeit soll ein neuartiges Konzept, das auf den Szintillationseigenschaften von gasförmigen N2 beruht, weiter entwickelt und ein Prototyp zur Messung der Radonkonzentration in Luft und N2 aufgebaut und charakterisiert werden.

Zur Verbesserung der Effizienz des Myontriggersystems des ATLAS-Detektors am Large Hadron Collider (LHC) werden neue schnelle Triggerdetektoren (Resistive Plate Chambers) gebaut, die sehr hohe Untergrundzaehlraten aushalten und hoehere Lebensdauer und Zeitaufloesung als bisher aufweisen. Die Eigenschaften dieser Detektoren werden in vorhandenen Testaufbauten und im Teststrahl am CERN systematisch untersucht.

Multifunctional materials combining nontrivial conducting and magnetic properties are one of hot topics in the modern condensed matter physics. Prominent examples are charge transfer salts of the organic donor BETS with anions containing a transition metal. They represent perfect multilayer structures of conducting and magnetic subsystems separated on a nanometer scale. The interplay between the two subsystems results in a variety of electronic and magnetic ground states depending on subtle chemical substitutions as well as on external magnetic field and pressure. The goal of this master thesis is a quantitative study of the interaction between the itinerant electrons and localized spins in the antiferromagnetic superconductors (BETS)₂FeX₄ with X = Cl, Br. To this end, comparative measurements of quantum oscillations in the magnetoresistance and magnetization will be carried out at strong magnetic fields, at temperatures down to 0.4 K. The results will be analyzed in terms of the exchange field dependent spin-splitting effect in a quasi-two-dimensional metal.

Physics: Correlated electronic systems; magnetic ordering and superconductivity; magnetic quantum oscillations.

Techniques: Strong magnetic fields; magnetotransport; magnetic torque; cryogenic (liquid 4He and 3He) techniques.

Contact person: Mark Kartsovnik (mark.kartsovnik@wmi.badw.de)

The computational design of new materials for heterogeneous catalysis relies on methods that can accurately predict adsorption energies of reactants and intermediates at low computational cost. Quantum-mechanical calculations based on the Density Functional Theory (DFT) are typically used for surface reactivity studies, but their cost significantly limits the number of compounds that can be evaluated as candidates for catalysing each targeted reaction. Scaling relations between adsorption energies of similar adsorbates and machine-learning approaches have been recently developed to circumvent this computational burden, but their application is currently limited to extended close-packed metal surfaces. Real catalysts are typically formed by supported metal nanoparticles exposing sites at different facets, edges, and corners. Dealing only with extended surfaces thus limits the predictive potential of computational catalyst design.

The goal of this M.Sc. project is to extend existing approaches for predicting adsorption energies to the description of undercoordinated sites typically found on metal nanoparticles. The candidate should be interested in electronic structure and machine learning methods, as well as in surface chemistry and catalysis. The student will learn how to perform DFT-based calculations and to use and asses the results of different machine learning methods. Basic knowledge of UNIX based operating systems and some experience with programming (and/or scripting) languages (preferably Python) is desirable, but not required.

Questions about the project can be directed to mie.andersen@ch.tum.de and albert.bruix@ch.tum.de. 

 Our Group at the Technische Universität München (TUM) studies the properties of hadronic interactions and their implications for astro-particle physics by means of accelerator experiments. 

One of the indirect ways to search for dark matter (c) is to look for 𝜒𝜒̅ annihilations resulting in final states such as 𝑝𝑝̅,𝑒+𝑒−,𝑑𝑑̅…with satellite or balloon experiments. In particular, low energy 𝑑̅ seem to be optimal candidates for such searches, since cosmic ray-induced background does not contribute too much to this final state. 

In order to estimate a reliable detection probability of dark matter-induced events such as 𝜒𝜒̅⟶𝑑𝑑̅+ .., the interaction probability of 𝑑̅ with normal nuclear matter must first be measured. The latter will indeed drive the detection probability of antiparticles in the spectrometers. 

Since the 𝑑̅+𝐴 (𝐴=𝐶,𝐴𝑙,𝑆𝑖..) elastic and inelastic cross-sections for 𝑑̅ with energies lower than 6 GeV is completely unknown, the topic of the advertised PhD deals with the measurement of these interactions. 

This is made possible by analyzing pp collisions at √𝑠=13 𝑇𝑒𝑉 at the LHC measured by the ALICE detector, since in these collisions a large statistics of 𝑝̅ and 𝑑̅ is produced and their interaction with the detector material can be studied. 

The Master Thesis work will be structured in the following way: 

* Determination of the  𝑝̅/p and  𝑑̅ /d experimental ratios as a function of the particle momentum, extracted from pp collisions measured by ALICE at  √𝑠=13 𝑇𝑒𝑉

* Comparison of the experimental ratio to GEANT4 simulations

* estimation of the 𝑑̅+𝐴 (𝐴=𝐶,𝐴𝑙,𝑆𝑖..) cross sections.

In this project, we aim to fabricate and investigate novel organic-inorganic hybrid materials for thermoelectric applications. The goal is to realize efficient low temperature (T < 100°C) thermoelectric thin films and coatings which can contribute for example to energy efficient buildings. By combining nanostructured inorganic materials with conducting polymers a novel approach for this class of materials shall be realized. Possible inorganic nanomaterial components include Silicon nanocrystals (either undoped, n-type or p-type doped) as well as other nanoparticles. Different polymer materials such as the polymer blends of conjugated polymers, which can be tuned in conductivity and in its nanostructure, shall be used as the organic partner in our hybrid approach.

Organic photovoltaic (OPV) technology has seen a rapid evolution over the last decade. The unique selling points of OPVs, such as excellent light harvesting capability, freedom of form, color and transparency, environmental friendliness, easy scalability and lower manufacturing costs based on roll-to-roll printing methods, position this technology for the mobile power market, and this most properly reflects the state of the art in commercialization. An important milestone towards OPV commercialization has been surpassed by reaching a power conversion efficiency (PCE) of up to 17%. The main limitation in OPVs is due to the intrinsic narrow absorption window (~100-200 nm) of polymers compared to inorganic semiconductors such as Si, which makes it challenging to fully cover the solar spectrum with a single junction device. To overcome the absorption limitation, ternary blend organic solar cells represent one of the dominant strategies that has been explored in the last decade. The outstanding advantage of ternary blends consists of maintaining the simplicity of the processing conditions used for single active layer devices. Interestingly, the open circuit voltage (Voc) is tunable depending on the loading concentration of the ternary compound in both ternary systems compromised of i) two donors and one accepter as well as ii) one donor – two accepters blends.

The origin of the composition-tunability of Voc and the optimal electronic correlations between the components remains as an open question in OPV field. Obviously, it is very challenging to develop a complete model for Voc in ternary systems mainly due to the fact that this model should take all the electronic interactions between the components into account and consider their role in the photogeneration. Moreover, the model should be compositional dependent and accounts for the different microstructures and transport mechanisms operating simultaneously in the system. To this end, a semi-empirical method is required to link the electrical characteristics of ternaries to their morphological properties and draw a comprehensive picture from the morphology models reported in literature as the origin of Voc changes in ternary systems.

This project will focus on solving the aforementioned key challenge by employing a semi-emprical method based on kinetic Monte Carlo (kMC). An existing lattice kinetic Monte Carlo model, previously applied to the modeling of binary polymer:fullerrene solar cells, will be extended to treat ternary blends. This will allow us to correlate nano-morphological features with measured optical properties and obtained Voc, a crucial capability for the design of optimized, high performance ternary solar cells. 

Quantum optics is an extremely powerful approach towards quantum communication, quantum sensing, and quantum computing. In particular, quantum information stored in photons has very low decoherence and can be transmitted over large distances through optical fibers. To date, most experiments in quantum optics use optical tables full with mirrors and beam splitters that all have to be carefully aligned and stabilized. This may be good enough for initial demonstrations, but in order to bring quantum science into the realm of quantum technology, a more scalable approach is required.

With our expertise in making photonic chips using advanced nanofabrication, we are making putting these exciting quantum optics experiments on chips. Here, light is routed via optical waveguides. Furthermore, by bending a waveguide, one gets the equivalent of a free-space mirror; a beam splitter cube becomes a directional coupler and so on. By combining these elements, we can make the building block for e.g. an optical quantum computer. With that, the possibilities are almost unlimited.

For such large-scale optical quantum circuits we also want to incorporate single-photon sources, superconducting single-photon detectors, and optomechanical phase shifters. This all happens on a single chip. Making and characterizing the components is the first step and from there on, you are making more and more complex quantum chips. You will be doing the nanofabrication in the cleanroom, and then use our optical measurement setups to see how each device is performing. Depending on your preference, it may also be possible to add a modelling component to the project.

See http://www.groups.ph.tum.de/en/qtech/openings/ for a detailed description of this project.

Optomechanics provides extremely sensitive methods to measure the displacement of mechanical resonators. However, the forces are much smaller in optomechanics compared to those in nanoelectromechanical systems (NEMS). The goal of the project is to make, and measure opto-electromechanical devices which have strong electrostatic interactions. This includes the electrostatic spring effect where the resonance frequency depends strongly on the applied voltage. The next step is trying to measure the potential by measuring the ringdown of different mechanical modes. The devices will be made using advanced nanofabrication techniques such as electron beam lithography and reactive ion etching.

See http://www.groups.ph.tum.de/en/qtech/openings/ for a detailed description of this project.

Viele Erweiterungen des Standardmodells sagen neue schwere Resonanzen voraus, die in Eichbosonpaare, ZZ, WW, WZ zerfallen. Mit Hilfe simulierter Daten werden die Selektionskriterien fuer diese Zerfaelle optimiert und die Nachweissignifikanzen bestimmt. Die Ergebnisse werden auf die neuesten Daten des ATLAS-Experiments bei der bisher hoechsten Proton-Proton-Kollisions- Energie von 13 TeV bestimmt.

Semiempirical electronic structure approaches like the density functional tight-binding (DFTB) method are popular due to their unrivaled computational efficiency. This often makes them the only option, e.g. for describing the electronic structure of nanoparticles, which may consist of tens of thousands of atoms. Their efficiency comes at the cost of limited accuracy and transferability, however. Currently, these shortcomings are compensated on a case-by-case basis by the introduction of empirical parameters. Unfortunately, even here the limitations of the underlying theory sometimes become evident.
The goal of this M.Sc. project is the development of a novel framework for a tight-binding-like theory that goes beyond the typical two-center approximation for the Hamiltonian. The candidate should be interested learning the fundamentals of electronic structure theory, implementing methods and performing numerical test of different approximations. The main focus of the work will be on programming. Experience with a scripting language (e.g. Python) is advantageous but not mandatory. Questions about the project can be directed to johannes.margraf@ch.tum.de.