Nye avanserte materialer har en nøkkelrolle å spille på ferden mot et mer bærekraftig samfunn.
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The Material Theory and Informatics group at NMBU specializes in using electronic structure calculations and molecular dynamics simulations. We also use machine learning to predict and gain insight into material properties and thermophysical relations.

About the Material Theory and Informatics group

Current research activities include the modelling of ferroelectric organic materials, physical properties of aqueous systems, , thermoelectric materials, theoretical spectroscopy, and complex processes such as electrical discharges in air. Several of these actives enjoy strong collaborations with experimental partners.

We also have a significant method development component relating to describing electronic band structure methods and van der Waals forces within the density functional theory framework.


Research areas

  • Our research employs a variety of machine-learning methods for material discovery and screening and property analysis. These include traditional methods like Random Forest and Gaussian Process Regression, as well as state-of-the-art techniques such as zero-shot, few-shot, cellular neural networks, self-supervised learning, and neural symbolic learning. We also use machine-learning force fields for first-principles molecular dynamics simulations.


  • Contact

  • The research is focused on quantifying the properties of novel gaseous dielectrics with low greenhouse warming potential using DFT and molecular dynamics.

    Treatment of large molecules in DFT to calculate electron swarm parameters (ionization and attachment rates etc.) is an active research topic.

    Molecular dynamics simulation is employed to model decomposition and reactions of gaseous dielectrics with the carrier gas (O2, N2 etc.) at high temperatures to capture species population and related thermodynamic and transport properties as a precursor for arc plasma modeling.


  • Process data technology is modelling and simulation based knowledge engineering and data management applied to problems from chemical and mechanical process technology. It is here applied to molecular dynamics (MD) and Monte Carlo (MC) simulation using quantitatively reliable classical-mechanical intermolecular pair potentials for fluids and the associated model development, with a focus on thermodynamic properties and processes at interfaces.



  • PhD students

    Master students

    Group alumni

    Rasmus André Tranås

    Rasmus Andre Tranås

    Former PhD student

    Helge Helø Klemetsdal

    Former master student

    Rakel Steensen

    Former master student

    Aditya Dey

    Former master student

  • Prospective master student?

    We offer master projects for students in physics, mechanical engineering, and data science with relevant course background. Get in touch early!

    Topics for potential master theses:

    • Atomistic modeling of materials.
      – Required course background: Basic knowledge of material science and quantum physics. It will be supplemented by courses at University of Oslo.
    • Quantum mechanical simulations and code implementation.
      – Required course background: Coding experience in numpy/scipy.
    • Material informatics.
      – Required course background: Data science (at least DAT200 level) and bases courses in material science (i.e. TBM200) or solid-state chemistry/physics.

    Seeking PhD or postdoc opportunities?

    All positions are announced on the NMBU web page, Psi-K and EURAXESS.

    NMBU offers yearly 'Postdoc Masterclass' for crafting supporting applications to the Marie Skłodowska-Curie actions.

    Potential scientific collaborator, or just interested?

    Do not hesitate to get in touch!