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Subject: search.filters.subject.530Publication Type: search.filters.UBSTyp.Abschlussarbeit (Master)Start date: 2022End date: 2022

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Now showing 1 - 5 of 5
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    Thermodynamical stability analysis of a model quasicrystal
    (2022) Holzwarth, Moritz
    The thermodynamical stability of a simple 2D model quasicrystal is analysed using the theory of the phason elastic free energy. Atoms in the crystal interact via a double-well potential called the Lennard-Jones Gauß-potenital. The essential mechanisms that support the quasicrystal's free energy are atom jumps called phasonic flips. The distribution of such flips in a crystal is computed in dependency of the crystal lattice, which is parameterized by a 2x2-matrix called the phasonic strain. This computation is fully analytic and is based on the popular cut-and-project-scheme for quasicrystals. The quasicrystal is found to be instable at low temperature but stabilized at high temperature due to large entropy. This is in accordance with an MD-simulation from 2008 that used the LJG-Interaction-potential for the first time.
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    Microwave properties of superconducting SrTiO3 at mK-temperatures
    (2022) Beydeda, Cenk
    In this thesis the properties of superconducting Nb-doped SrTiO3 are investigated, more concrete the optical conductivity was obtained as function of temperature, magnetic flux density and frequency. Superconducting Stripline resonators were used to probe the optical properties of Nb:SrTiO3. The optical conductivity of Nb:SrTiO3 reveals features that are typically associated with a dirty single-gap superconductor. At low frequencies the coherence peak predicted by the BCS theory is observed. In the type II superconductor Nb:SrTiO3 two critical magnetic flux densities are observed that correspond to two superconducting bands. The real part of the optical conductivity displays a strong initial increase in dependence of magnetic flux density even at lowest achieved temperature to values multiple times of the DC conductivity. The critical magnetic flux densities and the critical temperatures show a dome-shaped dependence on the Nb-doping.
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    Simulation studies of selective laser melting
    (2022) Gorgis, Azad
    The technology of SLM is used to layer three-dimensional functional components. Studying and refining the factors that influence the melting of Al layer. The layer is made up of six distinct Al atom sizes in the shape of a sphere (ball) with various diameters (40˚A, 80˚A, 160˚A, 220˚A, 440˚A, 880˚A). The simulation depends mainly on MD to simulate the melting process. Although the sample sizes change, system parameters must be scaled to accommodate two distinct sample sizes. The whole melting of the Al layer has been recorded, using both sample 1 (40˚A, 80˚A, 160˚A) and sample 2 (220˚A, 440˚A, 880˚A), where with and without Ar gas, to explore the influence of Ar in the system. It is expected that the findings of this study will serve as a platform for further research into complex systems with several layers, and that the methodological style used in this work will serve as a model for systematic studies into other structures. In the near future, this research might aid materials design for next-generation in 3D printing.
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    Multi-fidelity Bayesian machine learning for global optimization
    (2022) Kuchelmeister, Manuel
    The computational optimization and exploration of materials is a challenging task, due to the high dimensionality of the search space and the high cost of accurate quantum mechanical calculations. To reduce the number of costly calculations, the Bayesian Optimization Structure Search (BOSS) has been developed. BOSS combines sample-efficient active learning with Gaussian process regression. This work introduces several multi-fidelity approaches that can reduce the number of costly, accurate calculations even further by incorporating information from inexpensive but less accurate calculations. Using the intrinsic model of coregionalization, BOSS samples data from multiple atomistic calculations based on quantum chemistry (Gaussian16, using CCSD(T)), density-functional theory (FHI-aims, using a PBE-exchange correlation functional) and force fields (AMBER18). Multi-fidelity BOSS samples both, lower and higher-fidelity calculations, while maintaining CCSD(T) accuracy for the global minimum inference. We tested our new multi-fidelity approaches on a 4D alanine conformer search. There, multi-fidelity BOSS has reduced the computational cost, measured in CPU hours, by up to 90%. We found that the efficiency of the approaches depends mostly on the correlation and the computational cost difference between the fidelities. These tests serve as a benchmark for the great potential that multi-fidelity learning can have to reduce the cost of expensive structure-search problems.
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    Simulating stochastic processes with variational quantum circuits
    (2022) Fink, Daniel
    Simulating future outcomes based on past observations is a key task in predictive modeling and has found application in many areas ranging from neuroscience to the modeling of financial markets. The classical provably optimal models for stationary stochastic processes are so-called ϵ-machines, which have the structure of a unifilar hidden Markov model and offer a minimal set of internal states. However, these models are not optimal in the quantum setting, i.e., when the models have access to quantum devices. The methods proposed so far for quantum predictive models rely either on the knowledge of an ϵ-machine, or on learning a classical representation thereof, which is memory inefficient since it requires exponentially many resources in the Markov order. Meanwhile, variational quantum algorithms (VQAs) are a promising approach for using near-term quantum devices to tackle problems arising from many different areas in science and technology. Within this work, we propose a VQA for learning quantum predictive models directly from data on a quantum computer. The learning algorithm is inspired by recent developments in the area of implicit generative modeling, where a kernel-based two-sample-test, called maximum mean discrepancy (MMD), is used as a cost function. A major challenge of learning predictive models is to ensure that arbitrarily many time steps can be simulated accurately. For this purpose, we propose a quantum post-processing step that yields a regularization term for the cost function and penalizes models with a large set of internal states. As a proof of concept, we apply the algorithm to a stationary stochastic process and show that the regularization leads to a small set of internal states and a constantly good simulation performance over multiple future time steps, measured in the Kullback-Leibler divergence and the total variation distance.