Multiscale Modeling | Statistical Physics and Soft Matter

Directed Assembly of Soft Matter
Imagine your book case could construct itself without you moving a single finger. What might sound like science fiction, happens in fact every day in nature on a microscopic scale, for example when cell membranes are formed or you wash your dishes. Designing and engineering such self-assembling materials is one of the major challenges in soft matter, and holds immense promise for the large-scale fabrication of novel nanomaterials and pharmaceutics. In our group, we study the fundamental principles of these intricate systems using advanced theoretical and computational methods. In particular, we focus on the role of external fields on self-assembly, and the possibility to guide the building blocks into well-specified structures. For more information, contact Arash Nikoubashman.

Directed Assembly of Soft Matter

Imagine your book case could construct itself without you moving a single finger. What might sound like science fiction, happens in fact every day in nature on a microscopic scale, for example when cell membranes are formed or you wash your dishes. Designing and engineering such self-assembling materials is one of the major challenges in soft matter, and holds immense promise for the large-scale fabrication of novel nanomaterials and pharmaceutics. In our group, we study the fundamental principles of these intricate systems using advanced theoretical and computational methods. In particular, we focus on the role of external fields on self-assembly, and the possibility to guide the building blocks into well-specified structures. For more information, contact Arash Nikoubashman.

Hybrid Field-based Simulation Methods for Polymers
The so-called 'self-consistent field' (SCF) theory is one of the most successful density functional theories fo inhomogeneous polymer systems, which allows to calculate the local structure of dense blends at an almost quantitative level (see review article). We develop new hybrid simulation schemes for such systems that combine particle- and field-based representations of polymers, thus allowing to treat large parts of a system at the field level and zoom into certain areas in space with adaptive resolutions. Furthermore, we develop methods to combine different kinetic descriptions of polymeric fluids (diffusive Langevin and hydrodynamic Lattice-Boltzmann fields) in a consistent way. For more information, please contact Friederike Schmid .

Hybrid Field-based Simulation Methods for Polymers

The so-called 'self-consistent field' (SCF) theory is one of the most successful density functional theories fo inhomogeneous polymer systems, which allows to calculate the local structure of dense blends at an almost quantitative level (see review article).

We develop new hybrid simulation schemes for such systems that combine particle- and field-based representations of polymers, thus allowing to treat large parts of a system at the field level and zoom into certain areas in space with adaptive resolutions. Furthermore, we develop methods to combine different kinetic descriptions of polymeric fluids (diffusive Langevin and hydrodynamic Lattice-Boltzmann fields) in a consistent way. For more information, please contact
Friederike Schmid .

Memory Effects in Colloidal Systems
In soft matter, the separation of time scales is often incomplete and memory effects become important. We develop coarse-graining strategies for such situations, using the example of colloidal dispersions. We develop methods to reconstruct memory kernels in simple and complex fluids (e.g., electrolyte fluids). Our goal is to construct implicit solvent models that include memory effects and can be used for equilibrium and non-equilibrium simulations. In this context, we also develop algorithms for the efficient simulation of coupled generalized Langevin equations. For more information, please contact Friederike Schmid .

Memory Effects in Colloidal Systems

In soft matter, the separation of time scales is often incomplete and memory effects become important. We develop coarse-graining strategies for such situations, using the example of colloidal dispersions. We develop methods to reconstruct memory kernels in simple and complex fluids (e.g., electrolyte fluids). Our goal is to construct implicit solvent models that include memory effects and can be used for equilibrium and non-equilibrium simulations. In this context, we also develop algorithms for the efficient simulation of coupled generalized Langevin equations. For more information, please contact Friederike Schmid .