This award supports computational research and education in the interdisciplinary field of soft condensed matter, materials science, and biophysics. It focuses on the investigation of structural properties of polymers and proteins, with emphasis on structure formation processes such as folding, aggregation, and adsorption at substrates under thermal conditions. Various characteristic features of these transformations resemble phase transitions in macroscopic matter. However, the polymeric systems that are studied in this project are finite and sufficiently small for surface effects to be dominant or at least significant. Traditional theories of phase transitions typically do not include these effects, and therefore a revised approach is necessary. This is only possible by means of computer simulations. Sophisticated algorithms are used or still need to be developed and effectively replace mathematical methods in order to address the complexity of these systems.
In this project, generic models are developed and simulated that capture essential features of entire classes of soft materials. This is possible by systematic scans of spaces of material and external control parameters. The results are accumulated in what are called hyperphase diagrams, which contain information about all structures that can be exhibited by a generic model system. This is significant input for materials design, in particular for hybrid systems composed of soft and solid matter. Such systems are interesting from a nanotechnology point of view as they allow for a variety of biologically inspired applications such as sensors, transport on small scales, and photoreceptors.
Graduate students and postdoctoral researchers will be involved in this project and learn various skills beyond the mere acquisition and dissemination of knowledge in biophysics, polymer physics, and condensed matter physics. They are confronted with the efficient implementation of algorithms that run on highly parallelized computer systems, and statistical methods are needed to analyze the large data sets generated during the simulations. With these skills, the young scientists are well-prepared for the next stages in their career development.
This award supports computational research and education aimed to develop computational methodologies and statistical analysis tools for the understanding of structure formation processes and functions of macromolecular matter under thermal conditions. Generic, coarse-grained models are developed to investigate the structural behavior of entire classes of polymer systems and materials. Particular emphasis is dedicated to the study of the statistical mechanics of structural transitions of macromolecules. Employing recent advances in the development of statistical-physics based analysis tools such as the microcanonical inflection-point method, structural transitions that occur in finite systems can be investigated in a similar way as macroscopic phase transitions. The major research fields in this project are:
1. Generic approaches to conformational transitions. Monte Carlo computer simulations of coarse-grained polymer and protein models are used to understand the kinetics and thermodynamics of structural transitions in single flexible and semiflexible macromolecules for various model parameter settings that represent different soft materials.
2. Macromolecular aggregation processes. Aggregation processes and cluster formation are considered the potential cause of neurodegenerative diseases, but may also be potentially interesting from the perspective of materials design because of their flexibility and stability. Generic statistical properties of cluster formation will be studied by means of simulations of simplified coarse-grained models and microcanonical statistical data analysis.
3. Adhesion at substrates. In this project, the cooperative effects of the interaction between soft and solid matter are investigated. Of particular interest is understanding the processes that enable the macromolecular recognition of substrates with a particular surface structure or pattern.
The educational components of this award enable training of graduate students and postdocs in various interdisciplinary fields including molecular biology, polymer chemistry, computer science, and computational and statistical physics. This also includes the development of the ability to present and disseminate research results.
