Jonas Elm

Project title

Formation and Growth of Atmospheric Molecular Clusters

What is your project about?

The formation and early growth of atmospheric aerosol particles constitute one of the largest uncertainties in modelling of our current and future climate. This is caused by the fact that we do not understand how clusters on the scale of a single nanometer are capable of growing up to large aerosol particles that influence the climate. In this project we will lay the theoretical foundation for modelling the formation and growth processes of atmospheric molecular clusters. In particular, we will develop and apply novel machine learning techniques based on quantum chemical calculations. Our results will aid in understanding how aerosol particles are formed and will significantly improve our understanding of climate drivers at the molecular level.

How did you become interested in your particular field of research?

All the way back in primary school, I remember looking at the periodic table and finding it fascinating how different the chemical elements behaved, while only having a minor difference in the number of electrons, protons and neutrons. So even before I knew what quantum mechanics was it had already sprouted my interest. During my graduate studies, and in light of the societal challenge our climate represents, I became increasingly interested in atmospheric chemistry. Combining these two very different disciplines (quantum mechanics and atmospheric chemistry) has kept me increasingly curious during my scientific career.

What are the scientific challenges and perspectives in your project?

The application of machine learning techniques for studying the formation and growth of atmospheric clusters pose a significant scientific challenge. We will need to develop and test several machine learning techniques to identify which method that best represent our quantum chemical data and has the most efficient learning curves. Applying machine learning algorithms to quantum chemical data is an exciting and emerging research field that have major implications for data driven research in the years to come.

What is your estimate of the impact, which your project may have to society in the long term?

The project will give direct information into how atmospheric particles are formed and especially which chemical compounds that contribute to the process. From our calculations we can directly identify which chemical species that efficiently form particles and that should be regulated. We will implement our results in an atmospheric process model which will allow us to significantly reduce the uncertainty in current climate modelling. In the long run the project will be one of the first stepping stone towards implementing more rigorous chemical schemes in atmospheric models. Overall, this will give us a significantly improved understanding of our current and future climate.

Which impact do you expect the Sapere Aude programme will have on your career as a researcher?

The Sapere Aude programme will allow me to initiate my own independent research group focusing on computational atmospheric chemistry. This will aid me in coordinating and carrying out research at the highest international level. The programme will allow me to expand my research network and begin the training of excellent young researchers. I sincerely believe that I can contribute a great deal to understanding atmospheric aerosols from a molecular point of view and this prospect would definitely not be possible without the support from the Sapere Aude programme.

Background and personal life

In my spare time I enjoy hiking. It gives me a "free space" where I can efficiently clear my mind. As a theoretical chemist most of my ideas have actually sprouted from long walks rather than in the office.