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Computational Chemistry

The Computational Chemistry research group is led by Prof. Kari Laasonen and is currently the home of one postdoctoral researcher and four doctoral students. A short profile on each group member can be found here. The current status of job openings in the research group including summer employee positions can be checked on the Job Opportunities page.
Laasonen research group

Computational modelling has become an important research tool in the fields of chemistry and materials design, because it allows us to understand chemical phenomena such as catalysis at the fundamental level of atoms, electrons and molecules. In the future, the importance of computational modelling will continue to grow as increases in computational capacity will enable us to tackle larger and more complex problems. To meet these challenges, there are currently three research groups at the Department of Chemistry who are actively applying and developing new computational methods to study different problems in chemistry. In addition to the Computational Chemistry group, these include the Inorganic Materials Design group led by Prof. Antti Karttunen and the Novel Materials via Self-Assembly group led by Academy Research Fellow Maria Sammalkorpi.

The Computational Chemistry research group is one of the eleven groups that form the Finnish Centre of Excellence in Computational Nanoscience (COMP). COMP is an Academy of Finland funded flagship project, whose primary strategy is to apply and develop cutting-edge theoretical and computational models to tackle problems in condensed matter and material physics. The research topics of COMP are broad, ranging from ultracold Bose-Einstein condensates to nanoscale studies of friction, and from understanding the properties of hybrid inorganic-organic interfaces to developing new methods for studying multiscale soft-nano matter systems. For a detailed description of the ongoing research in other COMP groups, see here.

The unifying theme in all the research projects at the Computational Chemistry group is to understand how the electronic structure of atoms and molecules can be used to explain the chemical and physical properties of experimentally interesting systems. To investigate the fundamental properties of atoms, a quantum mechanical treatment is necessary. Because chemical processes are complex, our tool of choice is most often density functional theory, both in its time-independent and time-dependent formulations, where electron density is the basic quantity governing the behavior of atoms. This allows us to model systems upto thousands of atoms in size. The mathematical representations of these computational models are very demanding and solving them would not be possible without the supercomputing facilities at CSC - IT Centre for Science.

The Computational Chemistry group is responsible for arranging graduate (maisteri- ja tohtorivaiheen) level teaching on various lecture and laboratory courses in the new Master's Programme. The aim of the lecture courses is to establish the basics of statistical physics, quantum mechanics and quantum chemistry as needed by chemists, as well as to hone previously learned mathematical tools that are required to solve problems in these fields. In the practical hands-on exercise sessions and laboratory assignments, the focus is on giving the students an overview on the sort of systems that can be tackled with the tools of computational chemistry employing software that is in everyday research use in the group. A list of currently taught courses is given below, with additional information available either in Into(intranet) or in the school syllabus.

  • CHEM-E4110 Quantum Mechanics and Spectroscopy (5 cr)
  • CHEM-E4115 Computational Chemistry I (5 cr)
  • CHEM-E4225 Computational Chemistry II (5 cr)

Catalytic Properties of Magnetic Materials

Magnetism and magnetic materials have a wide array of applications ranging from medicine to electronics. Quantum mechanics can be used to explain the origins of magnetism in any material, because electrons have an intrinsinc magnetic dipole moment. Understanding the magnetic properties of individual atoms allows us to predict what kind of magnetic properties should be prevalent in bulk materials.

The magnetic state of a metal also affects the material's catalytic properties. By focusing on the CO dissociation reaction on iron nanoclusters and surfaces, our goal is to quantify these effects and to investigate whether magnetic fields could be used for controlling catalysis. This research is done in collaboration with the research groups of Prof. Hannes Jónsson (University of Iceland & Aalto University) and Prof. Jacinto Sá (University of Uppsala), as well as with Dr. Pavel Bessarab at the KTH Royal Institute of Technology, Sweden. In the future, our goal is to combine the understanding of magnetism with our experience in thermodynamic modelling of metal mixtures to discover novel alloyed nanoparticles with improved catalytic properties.

Thermodynamic Study of Platinoid Alloys with Ab Initio Methods

Alloys of the platinum group metals (PGMs) have wide variety of existing and potential applications in catalysis. However, much of the thermodynamic properties of alloys of the PGMs are yet unknown. Better understanding of the thermodynamic behavior and phase equilibria of the alloys could help in developing novel energy and lifecycle-efficient alloys and processes.

Although ab initio has been used to predict intermetallic phase diagrams for a long time, application to real materials in combination with experimental modelling methods (CALPHAD) is a rather new concept. Machine learning methods, such as genetic algorithms and heuristic structure selection, are applied in this project.

This project is done in collaboration with Prof. Pekka Taskinen's Metallurgical Thermodynamics and Modelling group at the Department of Materials Science and Engineering, Aalto University.

Computational Electrochemistry

Electrochemical devices, such as batteries and fuel cells, are key components in a wide variety of applications including portable electronics and transportation. To fully understand how these devices function, we need to be able to describe how electrons are transferred between molecular species, as this dictates the behavior of any electrochemical reaction or device exploiting this reaction.

Nowadays, density functional theory based computational modelling is routinely applied to gain atomic-level information in e.g. the development of more efficient catalysts or for verifying reaction mechanisms. However, the field of computational electrochemistry and -catalysis is still in its infancy due to the unique technical challenges associated with treating electron transfer reactions.

In the Computational Chemistry group, we are interested in electrochemical systems which are also relevant from the viewpoint of experimentalists. Examples include the hydrogen evolution and oxygen reduction reactions on pristine and doped carbon nanotubes. To achieve a comprehensive understanding of these systems, we actively collaborate with experimental electrochemistry research groups, for example, with the Electrochemical Energy Conversion and Storage group lead by D.Sc. Tanja Kallio.

Computational Modelling of Atomic-layer Deposition

Atomic layer deposition (ALD) is a coating technology used to produce highly uniform thin films. Most of the ALD research in done using conventional experiments. However, small time-scale surface reactions are difficult to measure. Computational quantum chemistry provides a tool to gain insight on the surface kinetics. Understanding the surface reactions and processes is essential in the design and optimization of ALD processes.

The key to bridge molecular simulations with real size processes is kinetic modelling. Homogeneous systems can often be broken down into few crucial, rate-limiting steps that can be modelled using macroscopic, text-book rate equations. However, heterogeneous systems - e.g. reactions on a surface - seldom fulfill the underlying assumptions of macroscopic rate equations. Adsorbed molecules often have lateral interactions and reaction energetics are dependent on the surface coverage. These kind of phenomena can be described by, for example, kinetic Monte Carlo simulations where a stochastic algorithm is used to explore the phase space of surface configurations. Transition rates between different configurational states can be calculated ab initio using density functional theory or post-Hartree-Fock theories. 

Non-Adiabatic Electron-Ion Dynamics of Excited States

Electrochemical reactions always involve a step where electrons are transferred from an electron donor to an electron acceptor. To understand the dynamics of this process, we need a tool that allows us to accurately treat excited states but is computationally not too demanding to prevent us from studying system sizes that are interesting from an experimental perspective. Time-dependent density functional theory has been shown to be a reasonable compromise between these two factors.

In this project, we are interested in a process where an organic molecule (carboxylic acid anchored perylene) donates electrons to a TiO2-anatase surface. Our goal is to determine which factors improve the surface's ability to accept electrons from the organic molecule. This project is a joint project between the Computational Chemistry group and Prof. Martti Puska's Electronic Properties of Materials group at the Department of Applied Physics.

The DEMEC Project

The energy production, conversion and storage are world wide issues. The traditional energy technology based on fossil fuels cannot be used solely and new technologies are needed. The most important primary energy source is the sun. Solar energy is intermittent by nature and thus, electrical energy has to be converted and stored so that it is available when needed. In a recent report by IEA, hydrogen has been recognized as one of the best energy vectors and storage medium. Hydrogen conversion technology is in principle scalable and thus, suitable for variety of applications.

The hydrogen conversion has several problems including the storage and the fact that the commercial fuel cells and electrolyzes utilizes expensive platinum group metals (PGM) as catalysts. To cover Pt demand for very common applications, such as cars, annual Pt production should rise fivefold. Under such a high market pressure, Pt price would become astronomical limiting its use. This is one of the key problems in the hydrogen utilization and the topic of the DEMEC project.

The energy production, conversion and storage are world wide issues. The traditional energy technology based on fossil fuels cannot be used solely and new technologies are needed. The most important primary energy source is the sun. Solar energy is intermittent by nature and thus, electrical energy has to be converted and stored so that it is available when needed. In a recent report by IEA, hydrogen has been recognized as one of the best energy vectors and storage medium. Hydrogen conversion technology is in principle scalable and thus, suitable for variety of applications.

The hydrogen conversion has several problems including the storage and the fact that the commercial fuel cells and electrolyzes utilizes expensive platinum group metals (PGM) as catalysts. To cover Pt demand for very common applications, such as cars, annual Pt production should rise fivefold. Under such a high market pressure, Pt price would become astronomical limiting its use. This is one of the key problems in the hydrogen utilization and the topic of the DEMEC project.

Group members

Geraldine Cilpa-Karhu

Department of Chemistry and Materials Science
Postdoctoral Researcher

Virve Karttunen

Department of Chemistry and Materials Science
Project Manager

Rasmus Kronberg

Department of Chemistry and Materials Science
Doctoral Candidate
Kari Laasonen

Kari Laasonen

Department of Chemistry and Materials Science
Professor

Garold Murdachaew

Department of Chemistry and Materials Science
Research Fellow

Latest publications

Computational Chemistry, Department of Chemistry and Materials Science, Department of Applied Physics

Atomistic simulations of early stage clusters in Al–Mg alloys

Publishing year: 2019 Acta Materialia
Department of Chemistry and Materials Science, Computational Chemistry

Hydrogen adsorption trends on various metal-doped Ni<sub>2</sub>P surfaces for optimal catalyst design

Publishing year: 2019 Physical Chemistry Chemical Physics
Computational Chemistry, Department of Chemistry and Materials Science

Oxygen Evolution Reaction on Nitrogen-Doped Defective Carbon Nanotubes and Graphene

Publishing year: 2018 Journal of Physical Chemistry C
Department of Chemistry and Materials Science, Computational Chemistry

Kinetic Monte Carlo Study of the Atomic Layer Deposition of Zinc Oxide

Publishing year: 2018 Journal of Physical Chemistry C
Department of Chemistry and Materials Science, Computational Chemistry

Oxygen Evolution Reaction Kinetic Barriers on Nitrogen-Doped Carbon Nanotubes

Publishing year: 2018 Journal of Physical Chemistry C
Department of Chemistry and Materials Science, Computational Chemistry

Ab initio molecular dynamics studies of formic acid dimer colliding with liquid water

Publishing year: 2018 Physical Chemistry Chemical Physics
Physical Charactristics of Surfaces and Interfaces, Department of Chemistry and Materials Science, Computational Chemistry, Department of Bioproducts and Biosystems, Bio-based Colloids and Materials, Department of Applied Physics, Surface Science, NanoMaterials, Electrochemical Energy Conversion

Experimental and Computational Investigation of Hydrogen Evolution Reaction Mechanism on Nitrogen Functionalized Carbon Nanotubes

Publishing year: 2018 ChemCatChem
Computational Chemistry, Department of Chemistry and Materials Science

Computational exploration of [email protected] Fischer-Tropsch synthesis

Publishing year: 2018 Physical Chemistry Chemical Physics
Department of Chemistry and Materials Science, Computational Chemistry

Hydrogen adsorption trends on Al-doped Ni 2 P surfaces for optimal catalyst design

Publishing year: 2018 Physical Chemistry Chemical Physics
Computational Chemistry, Department of Chemistry and Materials Science

Hydrogen Evolution Reaction on Carbon Nanotubes: Insights from Electronic Structure Theory

Publishing year: 2018
More information on our research in the Research database.
Research database