Universität Stuttgart
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Item Open Access Umgang mit Forschungssoftware an der Universität Stuttgart(2020) Flemisch, Bernd; Hermann, Sibylle; Holm, Christian; Mehl, Miriam; Reina, Guido; Uekermann, Benjamin; Boehringer, David; Ertl, Thomas; Grad, Jean-Noël; Iglezakis, Dorothea; Jaust, Alexander; Koch, Timo; Seeland, Anett; Weeber, Rudolf; Weik, Florian; Weishaupt, KilianWir empfehlen die Einrichtung einer Organisationseinheit Forschungssoftware-Entwicklung an der Universität Stuttgart und eines daran angegliederten Stellenpools von Research Software Engineers (RSEs). Dazu schlagen wir Maßnahmen zur Schaffung und Finanzierung entsprechender neuer RSE-Stellen, zur Integration bestehender Stellen sowie zur Gewinnung und Förderung geeigneter Personen vor. RSEs sind Personen, die sich um Konzeption, Organisation, Implementierung, Testen, Dokumentation und Wartung von Forschungssoftware kümmern. Die institutionelle Förderung von Forschungssoftware-Entwicklung ist notwendig, da die Bedeutung von Software für die Forschung und Anforderungen an die entsprechende Software, u.a. durch die DFG, stetig zunimmt.Item Open Access Hybrid molecules consisting of lysine dendrons with several hydrophobic tails : a SCF study of self-assembling(2023) Shavykin, Oleg V.; Mikhtaniuk, Sofia E.; Fatullaev, Emil I.; Neelov, Igor M.; Leermakers, Frans A. M.; Brito, Mariano E.; Holm, Christian; Borisov, Oleg V.; Darinskii, Anatoly A.In this article, we used the numerical self-consistent field method of Scheutjens-Fleer to study the micellization of hybrid molecules consisting of one polylysine dendron with charged end groups and several linear hydrophobic tails attached to its root. The main attention was paid to spherical micelles and the determination of the range of parameters at which they can appear. A relationship has been established between the size and internal structure of the resulting spherical micelles and the length and number of hydrophobic tails, as well as the number of dendron generations. It is shown that the splitting of the same number of hydrophobic monomers from one long tail into several short tails leads to a decrease in the aggregation number and, accordingly, the number of terminal charges in micelles. At the same time, it was shown that the surface area per dendron does not depend on the number of hydrophobic monomers or tails in the hybrid molecule. The relationship between the structure of hybrid molecules and the electrostatic properties of the resulting micelles has also been studied. It is found that the charge distribution in the corona depends on the number of dendron generations G in the hybrid molecule. For a small number of generations (up to G=3), a standard double electric layer is observed. For a larger number of generations (G=4), the charges of dendrons in the corona are divided into two populations: in the first population, the charges are in the spherical layer near the boundary between the micelle core and shell, and in the second population, the charges are near the periphery of the spherical shell. As a result, a part of the counterions is localized in the wide region between them. These results are of potential interest for the use of spherical dendromicelles as nanocontainers for drug delivery.Item Open Access Simulating stochastic processes with variational quantum circuits(2022) Fink, DanielSimulating 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.Item Open Access Coarse grained hydrogels(2017) Richter, Tobias; Holm, Christian (Prof. Dr.)Item Open Access Training robust and generalizable quantum models(2024) Berberich, Julian; Fink, Daniel; Pranjić, Daniel; Tutschku, Christian; Holm, ChristianItem Open Access MDSuite : comprehensive post-processing tool for particle simulations(2023) Tovey, Samuel; Zills, Fabian; Torres-Herrador, Francisco; Lohrmann, Christoph; Brückner, Marco; Holm, ChristianParticle-Based (PB) simulations, including Molecular Dynamics (MD), provide access to system observables that are not easily available experimentally. However, in most cases, PB data needs to be processed after a simulation to extract these observables. One of the main challenges in post-processing PB simulations is managing the large amounts of data typically generated without incurring memory or computational capacity limitations. In this work, we introduce the post-processing tool: MDSuite. This software, developed in Python, combines state-of-the-art computing technologies such as TensorFlow, with modern data management tools such as HDF5 and SQL for a fast, scalable, and accurate PB data processing engine. This package, built around the principles of FAIR data, provides a memory safe, parallelized, and GPU accelerated environment for the analysis of particle simulations. The software currently offers 17 calculators for the computation of properties including diffusion coefficients, thermal conductivity, viscosity, radial distribution functions, coordination numbers, and more. Further, the object-oriented framework allows for the rapid implementation of new calculators or file-readers for different simulation software. The Python front-end provides a familiar interface for many users in the scientific community and a mild learning curve for the inexperienced. Future developments will include the introduction of more analysis associated with ab-initio methods, colloidal/macroscopic particle methods, and extension to experimental data.