Computational Chemistry

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 and develop the performance of these devices, an atomistic description of the structure and electron transfer properties of the employed functional materials is required. Nowadays, density functional theory based computational modelling is routinely applied to gain nanoscale information in the rational design 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 at constant electrode potential conditions. Also the importance of solvation and interfacial dynamics remain to be thoroughly elucidated.

In the Computational Chemistry group, we are interested in electrochemical systems which are also relevant from the viewpoint of experimentalists. Examples include the hydrogen and oxygen evolution reactions on prototypical systems such as single-crystal platinum, as well as novel materials like pristine and modified carbon nanostructures and earth-abundant transition metal compounds (e.g. MoS₂, Ni₂P). We are also very much interested in the more fundamental aspects of interfacial electrochemistry, namely the structure of dynamic electrode-electrolyte interfaces as well as rigorous description charge transfer processes using formalisms such as constrained density functional theory (CDFT). In exploring the dynamics of electrochemical reactions and interfaces, we apply our solid expertise in performing ab initio molecular dynamics simulations.

To achieve a comprehensive understanding of these systems, we actively collaborate with experimental electrochemistry research groups, for example, with the Electrochemical Energy Conversion group lead by Prof. Tanja Kallio and the research group of Dr. Andy Wain at the National Physical Laboratory (NPL, UK).

Research highlights:

1. Reconciling the experimental and computational hydrogen evolution activities of Pt(111) through DFT-based constrained MD simulations
2. Oxygen evolution on metal‐oxy‐hydroxides: beneficial role of mixing Fe, Co, Ni explained via bifunctional edge/acceptor route

3. Revisiting the Volmer–Heyrovský mechanism of hydrogen evolution on a nitrogen doped carbon nanotube: constrained molecular dynamics versus the nudged elastic band method

4. Diabatic model for electrochemical hydrogen evolution based on constrained DFT configuration interaction

5. Electrochemical activation of single-walled carbon nanotubes with pseudo-atomic-scale platinum for the hydrogen evolution reaction

Atomic layer deposition (ALD) is a coating technology used to produce highly uniform thin films. Although most of the ALD research is conducted by conventional experiments, small time-scale surface reactions are difficult to probe. Computational quantum chemistry provides a complementary tool to gain refined insight on the surface kinetics. Understanding these surface reactions 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 a few crucial, rate-limiting steps that can be modelled using macroscopic, text-book rate equations. However, heterogeneous systems - e.g. surface reactions - seldom fulfill the underlying assumptions of macroscopic rate equations. Adsorbed molecules often have lateral interactions and reaction energetics are dependent on the surface coverage. Such phenomena can be described by, for example, kinetic Monte Carlo (kMC) 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.

Research highlights:

1. Kinetic Monte Carlo study of the atomic layer deposition of zinc oxide
2. First principles study of the atomic layer deposition of alumina by TMA–H₂O-process

Alloys of e.g. the platinum group metals (PGM) have a wide variety of existing and potential applications in catalysis. However, much of the thermodynamic properties of platinoid alloys, among others, remain unknown. Better understanding of the thermodynamic behavior and phase equilibria could help in developing novel energy- and lifecycle-efficient alloys and processes.

Although ab initio methods have 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 also applied in this project.

This project has been conducted in collaboration with Prof. Emeritus Pekka Taskinen at the Department of Chemical and Metallurgical Engineering, Aalto University, as well as Prof. Jaakko Akola (Norwegian University of Science and Technology, Tampere University of Technology).

Research highlights:

1. Atomistic simulations of early stage clusters in Al-Mg alloys

2. First-principles investigation of the Cu–Ni, Cu–Pd, and Ni–Pd binary alloy systems

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 intrinsic 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 catalytic properties of the material. By focusing on reactions involving e.g. CO on iron-based nanoclusters and surfaces, our goal has been to quantify these effects and to investigate whether magnetic fields could be used for controlling catalysis. We have also combined the gained understanding of magnetism with our experience in thermodynamic modelling of metal mixtures to study novel alloyed nanoparticles with improved catalytic properties.

A part of this research has been conducted in collaboration with Prof. Hannes Jónsson (University of Iceland, Aalto University).

Research highlights:

1. Fe–Ni nanoparticles: a multiscale first-principles study to predict geometry, structure, and catalytic activity
2. Effect of magnetic states on the reactivity of an FCC(111) iron surface

• For the current status of job openings including M.Sc. thesis positions related to currently active research projects, please contact Prof. Laasonen. Doctoral student candidates may apply at M.Sc. thesis level. B.Sc. thesis topics in physical chemistry with a focus on computational chemistry are available upon request.
• We hire annually 1-3 undergraduate (B.Sc. or M.Sc.) students as summer research assistants. The application period is in January.
• All applications with the appropriate attachments (e.g. curriculum vitae) should be sent to the research group leader, unless otherwise stated.