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Subject: search.filters.subject.530Institute: search.filters.UBSInstitut.Institut für ComputerphysikAuthor: search.filters.author.Tovey, Samuel

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Now showing 1 - 5 of 5
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    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, Christian
    Room-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.
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    Physics meets machine learning : theory and application
    (2024) Tovey, Samuel; Holm, Christian (Prof. Dr.)
    The doctoral thesis of Samuel Tovey. In this work, I explore the role machine learning plays in computational physics, specifically, the fitting of potential for molecular dynamics simulations and control of microscopic active matter. Further, it is shown that physics concepts can be used to understand machine learning, particularly the role of data in neural network training and the evolution and learning mechanisms of neural networks while training.
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    SwarmRL : building the future of smart active systems
    (2025) Tovey, Samuel; Lohrmann, Christoph; Merkt, Tobias; Zimmer, David; Nikolaou, Konstantin; Koppenhöfer, Simon; Bushmakina, Anna; Scheunemann, Jonas; Holm, Christian
    This work introduces SwarmRL , a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL , we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL .
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    MDSuite : comprehensive post-processing tool for particle simulations
    (2023) Tovey, Samuel; Zills, Fabian; Torres-Herrador, Francisco; Lohrmann, Christoph; Brückner, Marco; Holm, Christian
    Particle-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.
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    Self- and Fick diffusion coefficients in implicit solvent simulations : influence of local aggregation effects and thermodynamic factors
    (2025) Tovey, Samuel; Holm, Christian; Smiatek, Jens
    In this article, we discuss the relationship and transition between self- and Fick diffusion coefficients in continuous implicit solvents across different particle densities. By applying the established expressions for self-diffusion and Fick diffusion coefficients in binary solutions, we analyze how the local environment influences diffusion through thermodynamic factors, which can be readily evaluated within the framework of Kirkwood-Buff (KB) theory. These thermodynamic factors, originally defined as derivatives of thermodynamic activity, vary with changes in local particle densities, particularly in the presence of aggregation effects. Consequently, the transition from self- to Fick diffusion coefficients can be understood as a reflection of variations in these thermodynamic factors. Langevin Dynamics simulations at low number densities show excellent agreement with the analytical expressions derived. Overall, our findings provide deeper insight into how local structural environments shape particle dynamics, clarifying the connection between KB theory and the transition from self- to Fick diffusion coefficients.