In thermodynamics, we aim to increase the understanding of how energy conversion happens in renewable energy technologies. We also develop methods and principles to improve the energy efficiency, and theories to describe the fundamental properties of fluids and surfaces, both in large and small (nano) systems. This is crucial in areas such as carbon capture, transport and storage, hydrogen and battery technologies, as well as catalysis and surface and colloid chemistry.

In basic research, theoretical models are important because many of the interesting aspects take place on a scale that prevents the use of experimental tools. For example in quantum chemistry, the development of computational methods has reached a level where we can achieve higher accuracy in calculations, than what is possible in spectroscopic experiments.

Also in the industry, the interest for theoretical methods is significant. By using simulations and modelling one can achieve increased insight into chemical processes and systems that can be harnessed, while avoiding expensive and time-consuming experiments.

Working with theoretical chemistry, you will have the opportunity to investigate a broad range of chemical systems through a variety of methods and angles. Regardless of the branch of theoretical chemistry one belongs to, a theoretical investigation follows a relatively similar procedure:

• Development of a mathematical model that describes the process of interest
• Implementation of the mathematical model on a computer
• Applying the software to investigate a relevant system
• Analyze and process the large amounts of data generated

Students in our group can choose to focus on one or more of the steps in this chain. The knowledge and skills acquired are valuable in industry and research within all fields of interest.

You will find more about the research within the field of theoretical chemistry here.

In my master thesis work, I develop a model for thermal diffusion. In most applications, Fouriers law for heat transfer and Ficks law of diffusion are used separately. Thermal diffusion comes into play becasue these laws in realty are coupled. How strongly coupled they are varies between different mixtures and can be described by the Soret coefficient. To accurately model diffusion in systems with large temperature gradients, an accurate model for the Soret coefficient is required. As of today, no generally applicable and accurate model exists.

Molecular dynamics simulations can be used to “measure” the Soret coefficient, but they are computationally expensive. Therefore, it is not feasible to use simulations to measure all mixtures and conditions of interest. Experimental measurement of the Soret coefficient is both costly and difficult, and often gives results with high uncertainty. We are therefore working with the goal of developing a thermodynamic model that can predict the Soret coefficient in any mixture, in either the gas- liquid- solid- or supercritical phase. Of special interest is how thermal diffusion behaves in porous media. Such a model can be useful for describing anything from the transport of CO₂ in underground reservoirs to the decomposition of metallic alloys in gas turbines or electrolyte transport in batteries.

My work largely consists of deriving thermodynamic relations to investigate how different methods in thermodynamics work together. This can spin out and suddenly we end up in an almost philosophical place, asking ourselves questions such as “What is really an ideal gas, and how can we model it?”. After attempting to answer some theoretical questions, the thermodynamic relations are implemented using primarily Python, C++ and Fortran. The predictions by the thermodynamic model are tested against results from molecular simulations to find out if the model works, when it works and why it doesn’t work.

I think it’s exciting to work with a master thesis where I get the opportunity to use many of the skills I have developed throughout the integrated masters program. Every day, my knowledge within math, programming and chemistry is put to the test, that makes it extra fun when things work out.

Academic background: Bachelor in applied theoretical chemistry from UiB/NTNU

My project is about simplifying calculations when the goal is to eg. find the energy of large molecules.  Such calculations can often be very demanding, and it is therefore favorable to reduce the size of these calculations.  When presented with a large molecule, we are not always interested in computing the whole molecule with the same degree of accuracy.  What I am looking at is whether it is possible, using Hartree-fock theory, to divide the whole system into one active part (which we will compute with high accuracy) and a rest-part (which we will compute more crudely) and still get good results. 

My work combines computer modeling and theoretical work. I find this an interesting combination where I both get to keep in touch with my chemical background, but also learn new programming skills and obtain actual results.

• TKJ4170 Quantum chemistry
• TKJ4175 Chemometrics
• TKJ4200 Irreversible thermodynamics
• TKJ4205 Molecular modelling
• TKJ4215 Statistical thermodynamics in chemistry and biology
• KJ3021 Nuclear magnetic resonance spectroscopy
• KJ3053 Analytical methods for industrial and environmental monitoring
• TKP4175 Thermodynamic methods
• TFY4205 Quantum mechanics II
• TFY4210 Quantum theory of many-particle systems
• TFY4235 Computational physics
• TFY4255 Materials physics
• TFY4275 Classical transport theory
• TFY4280 Signal processing
• TFY4292 Quantum optics
• TFY4340 Nanophysics
• TFY4345 Classical mechanics
• TMA4145 Linear methods
• TMA4205 Numerical linear algebra
• TMA4300 Computer intensive statistical methods