Nettsider med emneord ?Computational quantum mechanics?

We use quantum mechanical theory (density functional theory) and develop statistical methods such as Monte Carlo techniques, molecular dynamics, thermodynamic integration, genetic algorithms in conjunction with machine learning to understand more about deep earth processes and core-mantle
interactions.

In HIDDEN we employ atomistic simulations (i) to establish the presence or not of a hidden geochemical reservoir in the deep mantle that can store noble gases, (ii) to calculate the permeability of the core-mantle boundary throughout geological time with respect to noble gases, (iii) to determine the exchanges of noble gases between the mantle and the core during the core formation, and (iv) to give estimates of fluxes of noble gases through the Earth¡¯s mantle throughout the geological time.

We develop and apply methods based on machine learning for chemistry and materials science. At the method level, our focus is on data (datasets computed with quantum mechanics methods), representations (graphs based on electronic structure theory), and models (graph neural networks and boosted trees).

For organometallic complexes to be useful as photosensitizers, it is important to know how the electron densities in the excited states are distributed in the molecular structures. In order to gain detailed insight into the electronic structures of the complexes, computational investigations were performed.

The conversion of CO2 to fuels using renewable energies involves catalysts that require further optimization for their large-scale implementation. In this project, DFT methods and microkinetic models are used to gain quantitative insight into the mechanism of these reactions and the steps governing catalytic activity and selectivity.

The selective methane oxidation to methanol represents a critical challenge due to the difficulty in activating the strong C?H bond of methane, preventing methanol oxidation. In this project, single-site catalysis based on metal organic frameworks (MOFs) will be developed taking natural enzymes based on copper as inspiration.

The WINNER project aims to develop an efficient and durable technology platform based on electrochemical proton conducting ceramic (PCC) cells designed for unlocking a path towards commercially viable production, extraction, purification and compression of hydrogen at small to medium scale.