Multiscale modeling and simulation of soft and biological matter in and out of equilibrium
Program leader: Prof. Dr. Matej Praprotnik
Soft and biological matter displays properties that span a wide range of spatiotemporal scales. Moreover, their interplay is often crucial for understanding of underlying physical and chemical mechanisms, with the aim to control and design new materials with desired properties. Owing to the limited computational power, however, computational studies of these complex molecular systems are still very challenging. Because of that, computational efficiency for simulating large systems on long time scales becomes one of the main targets in constructing modern simulation algorithms. Typically, in such molecular simulations, specific chemical details are only required in those domains, where a relevant process is unfolding, whereas the remainder of the system can be considered as a macroscopic thermodynamic bath. Hence, an efficient computational strategy consists in employing multiscale methods, which concurrently couple models with different resolution in different domains.
Soft and biological matter systems are commonly open, i.e., they exchange mass, momentum, and energy with their surroundings. State-of-the-art molecular simulations are on the other hand predominantly performed using periodic boundary conditions with a constant number of molecules. We aim to develop and apply advanced computational methods that will allow for open simulations of molecular liquids where the system exchanges mass, momentum, and energy with its exterior. This will enable us to perform efficient equilibrium molecular simulations in the Grand-Canonical ensemble and nonequilibrium fluid flow simulations. We will further extend this open setup to continuum description of fluids, solving the Navier-Stokes equation. Such hybrid approaches are especially useful for simulations of the transport of nanoparticles through fluids useful in target drug delivery.
As typical representatives of soft matter, linear polymers are composed of unbranched chains. Previous theoretical research has shown that liquid-crystalline polymers exhibit a geometric coupling between orientational order and density variations: a splay in the orientation induces a gradient in the density, and vice versa. We will apply this geometric reasoning to isotropic polymer systems and study to what extend gradients in the density can induce orientational order in a polymer melt, even if the equilibrium phase is isotropic. We will study the effect of acoustic waves or osmotic concentration gradients on observable orientational order in polymers. Our focus will be on linear biopolymers, e.g., DNA.
We will also combine statistical physics approached with modern computer techniques to concurrently couple atomistic with supramolecular models, where a coarse-grained bead corresponds to several molecules. We will resort to graph-based and machine learning approaches for systematic supramolecular coarse-graining and seamless coupling to the atomistic resolution.
Employing the developed methods, we will study various relevant problems in the field of biophysics, biology and (bio)chemistry, pharmaceutical and materials science.