You will become part of its vibrant community of enthusiastic researchers – BSc, MSc, and PhD students, postdocs, young researchers, and professors – and you will be part of its activities such as weekly seminars and centre meetings. We put emphasis on an open, friendly, collaborative, and supportive working environment and offer office space to MSc students at the Hylleraas Centre.
Our research covers broad areas of theoretical chemistry, spanning from mathematical analysis of quantum-chemical methods to multiscale modelling of complex biological systems, and we have extensive collaborations with experimental chemists within and outside the Department of Chemistry.
To find just the right MSc project, we invite you to come visit us at the second floor of the West wing of the chemistry building (the doors are unlocked throughout the day!) and talk to one or more of the potential MSc supervisors:
- Prof. Thomas Bondo Pedersen
- Prof. Michele Cascella
- Assoc. Prof. Simen Kvaal
- Dr. Erik Tellgren
- Dr. Ainara Nova Flores
- Dr. David Balcells
- Dr. Abril Castro Aguilera
MSc projects can for instance be in one of the following areas, with the possibility to combine subjects such as, for example, machine learning with computational catalysis or ultrafast processes with new methods for quantum dynamics. Come and talk to us and let us together define a master project that fits perfectly to your interests!
Computational Catalysis (Ainara Nova)
In computational catalysis we apply quantum chemistry methods, especially density-functional theory (DFT), to understand and predict catalysts and catalytic cycles. Master projects might be in
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Single-site catalysis:
- Homogeneous catalysts can be designed to be highly active and selective but suffer from robustness and recyclability. Single-site catalysis has the potential to overcome these limitations by offering the advantages of heterogeneous and homogeneous catalysts. We are applying DFT methods for (1) spectroscopy simulations enabling the characterization of the active sites, (2) reaction mechanism determination, including confinement effects, and (3) designing multifunctional catalysts based on metal-organic frameworks (MOFs) activating inert substrates like methane in highly selective processes.
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Catalytic CO2 Reduction:
- The conversion of CO2 to fuels by means of renewable energies involves catalysts that require further optimization for their large-scale implementation. We are using DFT methods and microkinetic models to gain quantitative insight into the mechanism of these reactions and the steps governing catalytic activity and selectivity. Further, we aim at designing catalysts able to perform multiple steps in an energy-efficient manner. We are interested in both homogeneous and single-site heterogeneous catalysts working under thermal and electrochemical conditions.
Computational NMR Spectroscopy of Catalytic Systems (Abril Castro)
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique to elucidate the structure of transition-metal complexes. Notably, the chemical shift also provides valuable insights into the electronic structure of these complexes, serving as a versatile descriptor of reactivity. Our research is focused on the modeling of NMR properties in organometallic compounds and materials relevant to catalysis. We employ robust computational protocols that incorporate environmental and relativistic effects for an accurate description of the spectra. By combining simulations with experimental data, we aim to i) unravel the structure of complex catalytic systems and ii) analyze the chemical shift tensor in terms of natural orbitals to gain a deep understanding of the NMR signatures.
Chemistry in Strong Magnetic Fields (Erik Tellgren)
Weak magnetic fields are important in for example NMR spectroscopy and strong magnetic fields, such as those found around Magnetic White Dwarf stars, give rise to many exotic effects like novel chemical bonds, rearrangement of electronic energy spectra, and complicated dynamics due to Lorentz forces. In this project magnetic field effects will be explored using computer simulations. You learn: theory related to magnetic fields in chemistry, programming.
Density-Functional Theory (Erik Tellgren)
The most commonly used quantum-chemical method is called density-functional theory. It has given rise to efficient models as well as visualization tools that are in widespread use in computational chemistry and materials science. In this project, these models or visualization tools will be studied with an eye towards further improving them. You learn: basics of density-functional theory, some scripting/programming.
Machine Learning for Chemistry (David Balcells)
You will work on machine learning applied to chemistry. Possible MSc projects will address problems in data generation and curation, the representation of complex chemical systems, and machine learning models for both prediction and generative tasks, including inverse design. You will be able to compute and process data in the state-of-the-art Norwegian supercomputers and to learn about graph theory for chemical representations. The group has expertise in diverse machine learning approaches, including deep learning methods, like neural networks, and Bayesian optimization methods, like Gaussian processes. We also work on the combination of machine learning with genetic algorithms. Besides method development, we do applications to chemical problems of interest, including catalysis for the green transition. In addition to communication and teamwork skills, you will develop your competences in programming, preparing yourself for the next position in academia or industry.
Multiscale Modelling (Michele Cascella)
We work on multiscale models of chemical systems in the condensed (liquid) phase using molecular dynamics. This means modelling the realistic motion of atoms and models during reactions and transformation, which can be visualised in a computer. Like in a microscope, multiscale models allow zooming in and out along the resolution scale, to observe molecular systems from their quantum-mechanical detail to their complex mesoscale dimensionality. Master projects can be in both methodological development and in applications:
- Development can involve data-driven approaches (machine learning / AI / Bayesian inference) for the accurate description of complex systems in solutions, with particular focus on large-scale soft matter systems (nano-micrometre dimensions).
- Application projects can involve the chemical reactivity of main-group organometallic systems (Grignard and organolithium reagents), as well as the self-assembly of surfactants and lipids, biological membranes, antimicrobial peptide, and the molecular modelling of biological complexes (protein/nucleic acids).
Applications projects likely involve the collaboration of experimental partner groups at KI (for example: Profs. Reidar Lund or Ute Krengel).
Ultrafast Processes and Quantum Control (Thomas Bondo Pedersen)
Advanced spectroscopic techniques based on ultrashort laser pulses – flashes of light with a duration down to a few attoseconds (10-18 seconds) – can be used to “see” and potentially control the motion of electrons in molecules and materials. We develop quantum-mechanical methods to study such laser-induced electron dynamics and apply them to investigate a variety of phenomena, including strong-field ionization, high-harmonic generation, light-induced electron diffraction, and time-resolved pump-probe spectroscopy. The same methods are also applied to study short-lived “bonds” formed between matter and anti-matter in collaboration with scientists at CERN, and to study the control of systems relevant to quantum technologies.
New Methods for Quantum Dynamics (Simen Kvaal)
Studying the quantum dynamics of molecules and electrons requires solving the time-dependent Schrödinger equation. However, this is an immense task, and achieving the high accuracy needed demands sophisticated—and sometimes entirely new—methods. Pursuing a MSc project in quantum dynamics is both challenging and rewarding. You will need strong analytical skills and programming expertise, as your code may run on supercomputers; with clever visualization, you will even be able to see the molecules in motion.
Machine Learning, Mathematics, and Electronic-Structure Theory (Simen Kvaal)
Electronic structure theories such as density-functional theory (DFT), Hartree–Fock, and coupled-cluster theory require extremely complex calculations. Yet, in chemistry, we often need to study molecular systems that exceed the capabilities of even these modern methods. Machine learning is now one of the most important tools for accelerating computational techniques. Moreover, it offers new ways to enhance methods—for example, by discovering mathematical formulas for approximations. An MSc project in this area combines analytical and mathematical skills with programming and supercomputing, teaching you how to build programs that explore spaces of mathematical functions and optimize them using genetic algorithms.