The goal of the project is to build models of how interaction energies depend on this tensor density. This will involve a mix of physical modelling and machine learning as well as development and implementation of supporting functionality within quantum-chemical software packages.
The project's primary aim is to understand, at a detailed molecular level, the influence of material structure and defects formed during synthesis and subsequent use, on the steam stability of functional microporous zeolite materials.
This project directly addresses a Grand Challenge for science in the 21st century: quantum control of electrons in atoms, molecules, and materials.
CO2 is a potential carbon source for chemical industry. In this project, we will study catalytic conversion of CO2 with H2 to form methanol and longer-chain compounds.
In this project, density functional theory (DFT) methods will be used to study the mechanism of CO2 hydrogenation reactions, including catalyst decomposition processes, which are barely known.
In this project, we will use density functional theory (DFT) calculations to study reaction mechanisms for CO2 and methane conversion reactions from reported or newly synthesized systems in the Catalysis group at the University of Oslo.
In this project the candidate will learn cutting-edge techniques from the fields of convex analysis and machine learning to develop new approaches to the kinetic energy of electronic systems — the key to unlocking the full potential of Density-functional theory.
Machine learning (ML) is revolutionizing the field of materials discovery. This project will fill the knowledge gap in the application of ML to homogeneous catalysis, which has been largely overlooked.
This project will develop and implement computational approaches to describe complex molecular systems, through the synergistic use of theoretical models at different resolutions.
The objective of this project is to employ advanced spectroscopic methods to understand and improve both the catalytic performance and stability of zeolite catalysts.