By simulating models of everything from the smallest particles to the entire cosmos, we gain fundamental insights into nature that can be difficult to obtain by experiment and theory alone.
By visualising our simulation results, we can make pictures or films that illustrate the fascinating physics behind complex phenomena: For example how electrons in certain proteins allow birds to sense the magnetic field, how molecules in rubber respond to deformation and hence how elasticity emerges at molecular scale, or how proteins transport molecules across the cell membrane. Visualisation is also an ideal tool for teaching and learning the physics of complicated systems. By simulating models of complex systems students can conduct virtual experiments and even change the fundamental laws of nature, and study how “our” universe emerges out of “our” particular laws of nature.
At SDU, our main focus is the physics of molecules of light and heavy atoms, cell membranes, nucleic acids, what makes living matter different from non-living matter, bird navigation, glassy systems and aging, soft-condensed materials, and ecosystems. These interdisciplinary topics touch on biology, materials science, artificial life, chemistry and sociology. We also collaborate with industry and apply our models to addressing practical problems such as improving drug delivery systems, rubber materials for car tires, or how milk turns into yoghurt and cheese.
We are unique in offering projects within such a wide range of computational topics to students. SDU also hosts the Abacus 2.0 supercomputer which has in excess of 14,000 cores. Abacus 2.0 is not only our laboratory for studying physics, but also the laboratory where we teach physics to students and where students work on their projects.
The Master's degree programme in Physics at SDU allows you to choose most of your courses according to your personal interests. In the academic year 2018/2019, we offer the following courses within the area of Particle Physics and Cosmology:
This course gives you insight into methods, models and phenomena in modern statistical physics. Apply relevant models of equilibrium and non-equilibrium phenomena in systems with many degrees of freedom. The goal is to apply relevant numerical methods, and validate and interpret the results of both simulations and theoretical analyses.
Responsible teacher: Michael Lomholt
The purpose of this course is to provide you with an overview of modern methods within the field of computational chemistry. Focus will be particularly on applications within organic chemistry.
Responsible teacher: Jacob Kongsted
The aim of the course is to enable you to perform and understand state-of-the-art electronic structure calculations for molecular properties.
Responsible teacher: Hans Jørgen Aagaard Jensen
The course introduces you to Information Theory and the Bayesian statistical framework, and applies it to solving inference problems, that is, how to judge how probable a given theory is when confronted with a certain set of observational data.
Responsible teacher: Michael Lomholt
The aim of the course is to enabley ou to understand and implement the particle-based molecular simulations of complex biological systems such as proteins, membranes and nucleotides, and interactions between them. In addition, the course aims to instill a quantitative understanding of biomolecular interactions, as well as to encourage and emphasize the importance of interdisciplinary research.
Responsible teacher: Himanshu Khandelia
Due to their fundamental nature, some of the courses listed above are also part of the specialisation in Soft Matter and Statistical Physics.
Master Thesis projects
The following are examples of possible Master Thesis topics within the area of Computational Physics:
- Model viscoelasticity of a biogel
- Development of models of polymer based drug delivery systems
- The statistics of non-equilibrium events in the Edwards-Anderson spin-glass
- The area-law as an emergent property of agent-based ecosystem models
- Rational design of molecular optical probes (J. Kongsted)
- Development of novel quantum chemistry embedding methods
- Simulations of Ion Transport across membranes
- Electro-mechanical coupling in biological membranes
- Physics of origins of life processes
- Technological evolution
- Modelling of partitioning processes of hydrophobic drugs in lipid/water systems
- In-silico synthesis of unimolecular soft nanoparticles under crowding
Who teaches Computational Physics?
Himanshu Khandelia: We employ and develop computational methods to investigate fundamental molecular scale biomolecular phenomena, with specific focus on simulations of membranes and membrane-associated biological processes, including transport across membrane proteins and interaction of membranes with membrane-active molecules.
Jacob Kongsted's main research field is centered around development of novel quantum-classical chemical approaches for the study of light-induced processes in complex biological materials with special focus on revealing the atomistic functioning of photoinduced processes in embedded systems.
Steen Rasmussen’s research focus is to analyze and understand the creative forces in natural and human-made systems. This is mainly done through computational and experimental studies of self-organizing processes. In physico-chemical systems this means assembling protocells bottom up from inorganic and organic materials. In hardware systems we investigate implementation of, for instance, 3D printers able to print themselves, while in computational systems we study the emergence of replicators. For socio-technical systems we use data analysis, mathematical modelling and simulations to investigate and understand the evolutions of technology.
Paolo Sibani works on the dynamics of complex non-equilibrium systems: within condensed matter, his focus is on disordered magnetic materials, for instance, spin glasses, and soft materials such as colloids, while in bio and social science models of interacting social agents are studied to understand the evolution of ecosystems. Computational models and simulations are key to understanding complexity, and have generated new theoretical ideas unifying the phenomenology of all the examples above.
Carsten Svaneborg is fascinated by the physics of everyday materials like rubbers, plastics and gels. He develops efficient computational models and simulates model materials. We not only learn how interesting material properties emerge out of molecular properties, but also use these insights to improve state-of-the-art theories and experimental analysis techniques.
Federica Lo Verso: Advances over the past decades have opened up the possibility of a predictive design of nano-materials using theory, modelling and simulations. Her research focuses on bio-inspired in-silico synthesis and hierarchical self-assembling of polymeric nanoparticles with different architecture and microscopic interactions (microgels, polymer-coated nanoparticles...). The development of new complex materials based on polymers and soft matter raises very interesting fundamental questions. The connection between morphology, dynamics and material properties, and the interplay between geometry and/or confinement are, among others, fundamental problems for future technological advancement based on such materials.