08 Fakultät Mathematik und Physik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9
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Item Open Access Efficient algorithms for electrostatic interactions including dielectric contrasts(2013) Arnold, Axel; Breitsprecher, Konrad; Fahrenberger, Florian; Kesselheim, Stefan; Lenz, Olaf; Holm, ChristianCoarse grained models of soft matter are usually combined with implicit solvent models that take the electrostatic polarizability into account via a dielectric background. In biophysical or nanoscale simulations that include water, this constant can vary greatly within the system. Performing molecular dynamics or other simulations that need compute exact electrostatic interactions between charges in those systems is computationally demanding. We review here several algorithms developped by us that perform exactly this task. For planar dielectric surfaces in partial periodic boundary conditions, the arising image charges can be either treated with the MMM2D algorithm in a very efficient and accurate way, or with the ELC term that enables the user to use his favorite 3D periodic Coulomb solver . Arbitrarily shaped interfaces can be dealt with using induced surface charges with the ICC algorithm. Finally, the local electrostatics algorithm MEMD (Maxwell Equations Molecular Dynamics) allows even to employ a smoothly varying dielectric constant in the systems. We introduce the concepts of these three algorithms, and an extension for the inclusion of boundaries that are to be held fixed at constant potential (metal conditions). For each method, we present a showcase application to highlight the importance of dielectric interfaces.Item Open Access Collective variables in data-centric neural network training(2023) Nikolaou, KonstantinNeural Networks have become beneficial tools for physics research. While they provide a powerful tool for data-driven modeling, their success is accompanied by a lack of interpretability. This thesis aims to add transparency to the opaque nature of NNs by means of collective variables, a concept well-known in the field of statistical physics. Three collective variables are introduced that emerge from the interactions between neurons and data. These observables enable one to capture holistic behavior of the network and are used to conduct an analysis of neural network training, focusing on data. Through the investigations, the collective variables are applied to selections from a novel sampling method: Random Network Distillation (RND). Besides studying collective variables, the investigation of Random Network Distillation as a data selection method composes the second part of this thesis. The method is analyzed and optimized with respect to its components, aiming to understand and improve the data selection process. It is shown that RND can be used to select data sets that are beneficial for neural network training, giving rise to its application in fields like active learning. The collective variables are leveraged to further investigate the selection method and its effect on neural network training, revealing previously unknown properties of RND-selected data sets. The potential of the collective variables is demonstrated and discussed from a data-centric perspective. They are shown to be discriminative towards the information content of data and give rise to novel insights into the nature of neural network training. In addition to fundamental research on neural networks, the collective variables offer several potential applications including the identification of adversarial attacks and facilitating neural architecture search.Item Open Access A brief review of capillary number and its use in capillary desaturation curves(2022) Guo, Hu; Song, Kaoping; Hilfer, R.Capillary number, understood as the ratio of viscous force to capillary force, is one of the most important parameters in enhanced oil recovery (EOR). It continues to attract the interest of scientists and engineers, because the nature and quantification of macroscopic capillary forces remain controversial. At least 41 different capillary numbers have been collected here from the literature. The ratio of viscous and capillary force enters crucially into capillary desaturation experiments. Although the ratio is length scale dependent, not all definitions of capillary number depend on length scale, indicating potential inconsistencies between various applications and publications. Recently, new numbers have appeared and the subject continues to be actively discussed. Therefore, a short review seems appropriate and pertinent.Item Open Access Machine learning-driven investigation of the structure and dynamics of the BMIM-BF4 room temperature ionic liquid(2024) Zills, Fabian; Schäfer, Moritz René; Tovey, Samuel; Kästner, Johannes; Holm, ChristianRoom-temperature ionic liquids are an exciting group of materials with the potential to revolutionize energy storage. Due to their chemical structure and means of interaction, they are challenging to study computationally. Classical descriptions of their inter- and intra-molecular interactions require time intensive parametrization of force-fields which is prone to assumptions. While ab initio molecular dynamics approaches can capture all necessary interactions, they are too slow to achieve the time and length scales required. In this work, we take a step towards addressing these challenges by applying state-of-the-art machine-learned potentials to the simulation of 1-butyl-3-methylimidazolium tetrafluoroborate. We demonstrate a learning-on-the-fly procedure to train machine-learned potentials from single-point density functional theory calculations before performing production molecular dynamics simulations. Obtained structural and dynamical properties are in good agreement with computational and experimental references. Furthermore, our results show that hybrid machine-learned potentials can contribute to an improved prediction accuracy by mitigating the inherent shortsightedness of the models. Given that room-temperature ionic liquids necessitate long simulations to address their slow dynamics, achieving an optimal balance between accuracy and computational cost becomes imperative. To facilitate further investigation of these materials, we have made our IPSuite-based training and simulation workflow publicly accessible, enabling easy replication or adaptation to similar systems.Item Open Access Tuning the properties and microstructuring of ionic liquid mixtures at surfaces through atomistic modeling(2021) Kobayashi, Takeshi; Fyta, Maria (Prof. Dr.)Item Open Access Percolativity of porous media(2022) Hilfer, Rudolf; Hauskrecht, J.Connectivity and connectedness are nonadditive geometric functionals on the set of pore scale structures. They determine transport of mass, volume or momentum in porous media, because without connectivity there cannot be transport. Percolativity of porous media is introduced here as a geometric descriptor of connectivity, that can be computed from the pore scale and persists to the macroscale through a suitable upscaling limit. It is a measure that combines local percolation probabilities with a probability density of ratios of eigenvalues of the tensor of local percolating directions. Percolativity enters directly into generalized effective medium approximations. Predictions from these generalized effective medium approximations are found to be compatible with apparently anisotropic Archie correlations observed in experiment.Item Open Access Simulation of novel magnetic materials in the field of soft matter(2014) Weeber, Rudolf; Holm, Christian (Prof. Dr.)This thesis has dealt with the tailoring of magnetic soft matter. Two strategies are available to achieve this goal. First, it is possible to alter the magnetic nanoparticles, in order to change their interactions. Second, it is possible to exchange the carrier fluid into which the magnetic particles are embedded by a more complex matrix. For each of these two possibilities, an example was studied, namely shifted-dipole particles and magnetic gels. Shifted-dipole particles (sd-particles) are a special kind of model magnetic particles which can be used to explain findings for particles with magnetic caps as well as for particles with magnetic inclusions. Magnetic gels, on the other hand, derive their particular properties from an interplay of the magnetic properties of the nanoparticles and the elastic behavior of the polymer matrix. In the thesis, the findings for these two systems will be discussed, and further research questions will be identified.Item Open Access Modeling the translocation of DNA structures through nanopores(2021) Szuttor, Kai; Holm, Christian (Prof. Dr.)Item Open Access On extremal domains and codomains for convolution of distributions and fractional calculus(2022) Kleiner, T.; Hilfer, R.It is proved that the class of c-closed distribution spaces contains extremal domains and codomains to make convolution of distributions a well-defined bilinear mapping. The distribution spaces are systematically endowed with topologies and bornologies that make convolution hypocontinuous whenever defined. Largest modules and smallest algebras for convolution semigroups are constructed along the same lines. The fact that extremal domains and codomains for convolution exist within this class of spaces is fundamentally related to quantale theory. The quantale theoretic residual formed from two c-closed spaces is characterized as the largest c-closed subspace of the corresponding space of convolutors. The theory is applied to obtain maximal distributional domains for fractional integrals and derivatives, for fractional Laplacians, Riesz potentials and for the Hilbert transform. Further, maximal joint domains for families of these operators are obtained such that their composition laws are preserved.Item Open Access Lattice Boltzmann simulations of fluid flow in the vicinity of rough and hydrophobic boundaries(2010) Kunert, Christian; Harting, Jens (P.D. Dr.)In recent years, it became possible to perform very well controlled experiments that have shown a violation of the no-slip boundary condition in sub-micron sized geometries. Since then, mostly experimental, but also theoretical works, as well as computer simulations, have been performed to improve our understanding of boundary slip. The topic is of fundamental interest because it has practical consequences in the physical and engineering sciences as well as for medical and industrial applications. This work focuses on numerical investigations of the slip phenomenon by means of lattice Boltzmann simulations with a strong focus on roughness and the interplay between roughness and wetting phenomena. To do so, two different slip measurement methods are simulated. One is to apply a Poiseuille flow between two patterned boundaries, and to record the flow profile. Then, the profile can be compared to the theoretical one, which assumes a slip boundary condition. The second method records the drag force that is acting on a sphere which is moved with a constant velocity towards the observed surface. Due to the influence of the boundary, the drag force acting on the sphere is disturbed and a correction function is needed to describe the measured force.