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.
Highlights
Aug 2023
Joining COST action "EuMINe"
Martin Horsch joins the management committee of "CA22143 - European Materials Informatics Network (EuMINe)"
Joining COST action "DAEMON"
Kristian Berland joins the management committee of "CA22154 - Data-driven Applications towards the Engineering of functional Materials: an Open Network (DAEMON)"
17 Feb 2023
Rasmus Tranås successfully defended his PhD thesis
Rasmus André Tranås, Faculty of Science and Technology (REALTEK) successfully defend his PhD thesis "Identifying Materials with Low Lattice Thermal Conductivity Using Machine Learning and Computational Modeling" on 17 February 2023
Jul 2020
van der Waals forces in materials: VdW-DF3
The group developed a new generation of van der Waals functionals, by utilizing an earlier overlook degree of freedom in the functional construction. The new functional is highly accurate for dispersion bonded systems, but can also describe covalent bonded systems accurately. It is available in the latest version of Quantum Espresso. The updated framework has potential to serve as a springboard for further development.
Completed masters theses
- Helge Helø Klemetsdal (2023): Machine learning force field simulations of the hybrid organic-inorganic perovskite system.
- Dey, Aditya (2023): Prediction of Melting Temperature of Organic Molecules using Machine Learning.
- Steensen, Rakel (2023): First-principles calculation of germanium telluride at the phase transition.
- Grimenes, Øven Andreas (2021): Screening of thermoelectric performance of half-Heusler materials and their alloys from first principles.
Completed PhD theses
- Tranås, Rasmus Andre (2023): Identifying Materials with Low Lattice Thermal Conductivity Using Machine Learning and Computational Modeling
- FOX – New ferroelectric organic molecular crystals through computer design and experimental realization (RCN FRINATEK, 2020-2024)
- Plastic additives: Integrity and cohesion (BASF, 2022-2026)
- Allotherm - High-throughput alloy design of superior thermoelectric materials (RCN FRINATEK, 2021-2025. Coordinated by SINTEF)
- MORTY – Momentum resolved electron band structures from electron energy loss spectroscopy. (RCN FRINATEK, 2021-2026. Coordinated by SMN, UiO)
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:
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.
Contact:
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.
Contact:
Contact
PhD students
Master students
Group alumni
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!
- Atomistic modeling of materials.