Introducing our WISE Guest Professors

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During his distinguished career, Prof. Katsnelson has received several prominent awards including the Spinoza Prize, the Hamburg Prize for Theroretical Physics and Knight of the Order of the Netherlands Lion. He is also Honorary Doctor at Uppsala University and elected member of the Royal Society of Sciences in Sweden and the Royal Netherlands Academy of Arts and Sciences. His main research interests are quantum many-body theory, statistical physics, magnetism, graphene and other two-dimensional materials.

What is the duration of your visit?
My visit will start on October first, and it will last a month.

Are there any research activities planned already?
I will work with the group of Prof. Olle Eriksson at Uppsala University, mostly on quantum many-body systems and magnetism out of equilibrium. Travels to other Swedish Universities in WISE, e.g.
KTH, and Stockholm and Linkoping Universities are also planned.

Could you share your thoughts on the challenges materials science faces regarding sustainability?
I think theoretical materials science is now at a very exciting and promising stage of its development. Thirty years ago, we had problems with describing, explaining and predicting properties of specific materials. The method used at that time, the so-called density functional method had serious problems with applications to materials with strong electron-electron interactions. The most interesting materials belong to this class, including all magnetic materials, unconventional superconductors, systems with metal-insulator transitions providing a high tunability, etc.

About twenty-five years ago, Sasha Lichtenstein and I suggested an approach which allowed to merge first-principle, quantitative treatment of specific materials with model-based treatment of the effects of strong electron-electron interactions.

During our long term and fruitful collaboration with Olle Eriksson and his group, we developed this approach and applied it to a particular group of materials. Next developments assume consideration of material properties out-of-equlibrium as well as the use of very powerful tools of machine learning and their combination with the methods developed before. They can result in a breakthrough in the design of novel and sustainable materials with desirable properties.