Hidde Vuijk
I am a PhD student in the Theory of Polymers department at the Leibniz-Institut für Polymerforschung in Dresden, Germany. My research mainly concerns problems in nonequilibrium statistical mechanics and is based on a combination of theory and simulations.
For a current list of publications, see my Google Scholar page.
Email: vuijk@ipfdd.de
RESEARCH
Active Matter
Active Brownian Particles under Lorentz Force
In the presence of a magnetic field gradient, a macroscopic flux emerges from a flux-free system of charged active (self-propelled) Brownian particles and the density distribution becomes inhomogeneous. The flux is induced by the gradient of the field only and does not require additional symmetry breaking such as density or potential gradients. This stands in marked contrast to similar phenomena in condensed matter such as the classical Hall effect which requires a macroscopic velocity vector.
Related publications:
H.D. Vuijk, J.U. Sommer, H. Merlitz, J.M. Brader and A. Sharma, Lorentz forces induce inhomogeneity and fluxes in active systems. Phys. Rev. Research , 2(1), 013320 (2020) [arXiv]
Chemotactic Behaviour of Active Particles
The target finding probability of active (self-propelled) Brownian particles depend strongly on the distribution of the 'fuel' they need. Fuel gradients result in a drift of particles up the gradient, resembling chemotaxis. This phenomena appears even if there is no coupling between the orientation of the particle and the fuel gradient.
Related publications:
H.D. Vuijk, A. Sharma, D. Mondal, J.U. Sommer and H. Merlitz, Pseudochemotaxis in inhomogeneous active Brownian systems, Phys. Rev. E 97 (4), 042612 (2018) [arXiv]
H. Merlitz, H.D. Vuijk, J. Brader, A. Sharma and J.U. Sommer, Linear response approach to active Brownian particles in time-varying activity fields, J. Chem. Phys. 148 (19), 194116 (2018) [arXiv]
H. Merlitz, H.D. Vuijk, R. Wittmann, A. Sharma and J.U. Sommer, Pseudo-chemotaxis of active Brownian particles competing for food. Plos One , 15(4), e0230873 (2020) [arXiv]
Lorentz Force in Diffusive Systems
The overdamped Langevin equation is a convenient starting point for many (non)equilibrium statistical mechanics problems. Besides the analytical advantages, it also speeds up simulation significantly. However, if a Brownian particle is subjected to a Lorentz force, the overdamped equation of motion, obtained by taking the small-mass limit, can not be used to determine the flux in the system because the limiting procedure leads to fluxes in the system that are not consistent with thermal equilibrium. The correct flux can, however, be calculated by different methods. This flux can be decomposed in a diffusive flux (coming from the symmetric part of the diffusion tensor) and a nondiffusive flux (coming from the asymmetric part of the diffusion tensor).
Related publications:
H.D. Vuijk, J.M. Brader and A. Sharma, Anomalous fluxes in overdamped Brownian dynamics with Lorentz force, J. Stat. Mech.: Theory Exp 2019 (6), 063203 (2019) [arXiv]
I. Abdoli, H.D. Vuijk, R. Wittmann, J.U. Sommer, J.M. Brader, A. Sharma, Stationary state in Brownian systems with Lorentz force, Phys. Rev. Research, 2(2), 023381 (2020)
I. Abdoli, H.D. Vuijk, J.U. Sommer, J.M. Brader, A. Sharma, Nondiffusive Fluxes in Brownian System with Lorentz Force, Phys. Rev. E , 101(1), 012120 (2020) [arXiv]
Previous Research
Nonequilibrium phase transition in driven particles
This research was done for my bachelor project. For which I worked, with F.C. Mackintosh and A. Sharma, on a model of driven particles on a one-dimensional lattice (the TASEP model) and studied the nonequilibrium phase transition of the flux and density distribution using theoretical methods and simulations.
Related publications:
H.D. Vuijk, R. Rens, M. Vahabi, F.C. MacKintosh, A. Sharma, Driven diffusive systems with mutually interactive Langmuir kinetics, Phys. Rev. E 91 (3), 032143 (2016) [arXiv]
The TASEP model. Particles are injected on the left most site and move to the right. In the bulk the attach and detach. The only interaction between the particles is a hard-core repulsion.
Dynamics in Random Neural Networks
The main task of the brain is to process information. For example, information enters the brain through the senses and is processed in the cognitive areas. One of the difficulties the brain has to deal with is that events and objects in the world have a wide range of time and length scales. When one listen to a story, the words need to be processed individually but also in context of a sentence. Similarly, in order to under stand a paragraph one needs to understand the sentences separately and in context of the paragraph. Recently, experimental studies have put forward evidence that the brain uses a processing hierarchy in order to makes sense of information with a wide range of time and length scales. This suggests that hierarchical processing is a basic principle in the brain. In this research I investigated what governs the time scale of random neural networks with chaotic dynamics and how this is related to the balance between excitation and inhibition in the network, and the dynamical critical transition from a quiescent to a chaotic state.
The activity of a neuron in a random neural network. As the synaptic strength is increased there is a transition from periodic (top) to quasiperiodic (middle) and finally chaotic (bottom) dynamics. In an infinite network, this would be an abrupt transition from a quiescent to a chaotic state.
Lecture Notes
Introduction to Statistical Mechanics of Liquids
Summer semester of 2018 and 2019 (together with A. Sharma).
This course is an introduction to basic concepts in statistical mechanics of liquids, such the pair-correlation functions, potential of mean force, and numerical solutions to integral equations.
Lecture notes and Python programs will be posted soon.
Active Matter and the Motility Induced Phase Transition
Summer semester 2019, as part of the course Special Topics in Statistical Physics at the TU Dresden.
These lectures are an introduction to active matter, both single particle behaviour and MIPS in interacting systems.
Lecture notes will be posted soon.