Public defence, Chemistry and Materials Science, M.Sc. (Tech) Mario Mäkinen

Title of the thesis: Density functional theory-based screening of small-molecule adsorption on transition metal and zinc oxide surfaces 

Thesis defender: Mario Mäkinen 
Opponent: Prof. Karoliina Honkala, University of Jyväskylä
Custos: Prof. Kari Laasonen, Aalto University School of Chemical Engineering

Utilization of modern supercomputing resources in comparison of surface reaction pathways

The energy required to synthesize products from the starting materials is relatively slow to acquire computationally when compared to experimental methods. Therefore, this energy is often excluded from computational chemical models, which noticeably lessens their ability to forecast real world results. However, given the enormous growth in the computational power of modern supercomputing, the capabilities to include this energy in computational models is a very relevant topic of an investigation. This doctoral research focused on acquiring the required energy to synthesize products, which is called activation energy, for surface reactions.

This research focused on the investigation of small molecule adsorption on pure metal surfaces and thin films consisting of various zinc oxide structures. By utilizing modern supercomputing resources, the research could acquire enough data for numerous surface reactions, in order to develop causal relationships between them. The investigation of the activation energy for adsorption on pure metal surfaces is sufficiently fast with modern resources, which makes the bottleneck the lack of suitable pure metals for a given application. Therefore, the research should focus on the investigation of various adsorbing molecules, while only focusing on few surfaces relevant for the application. In contrast, the surface reactions occurring on metal oxides are computationally too slow to be utilized in comparative studies. Therefore, during this research, additional simpler molecular models were created to gather comparative data from numerous reactions. This method allowed the discovery of trends in the reactivity of starting materials and thin films’ durability towards humidity.

The gathered information on the capabilities of modern computational methods can be utilized when screening methods become more popular in the field of surface science. Additionally, this information can be utilized in the future by creating a model from the computational data with machine learning, which further increases the predictive power of these models. When the predictive power reaches a sufficient level, the applicability of various surface reactions can be discovered without the need for expensive and sometimes dangerous experimental laboratory methods.

Keywords: DFT, screening, adsorption, transition metal, metal oxide

Thesis available for public display 7 days prior to the defence at Aalto University's public display page.

Contact information: 
https://www.linkedin.com/in/mario-makinen/