Sapere Aude research leader grant to iClimate researcher Jonas Elm
iClimate researcher Jonas Elm has been awarded 6.2 Mill. DKK for his research project entitled 'Formation and Growth of Atmospheric Molecular Clusters'. In his research, Jonas aims at combining knowledge of quantum mechanics, atmospheric chemistry and machine learning in order to better understand how particles are formed and grown in the atmosphere.
Jonas Elm, postdoc at the Department of Chemistry at AU ST, is a scientist at the iClimate center who is conducting relevant research especially within pillar 1 “Climate Drivers”. Now, Jonas has been awarded 6.2 mio DKK for his research project entitled 'Formation and Growth of Atmospheric Molecular Clusters'. Thereby, he is one of 11 AU researchers - 6 from ST – to receive Sapere Aude research leader grants from the Independent Research Fund Denmark. In his project, Jonas deals with quantum mechanics, atmospheric chemistry and machine learning to study how particles are formed in the atmosphere.
About research
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 it is not well understood 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, research leader Jonas and his colleagues will lay the theoretical foundation for modelling the formation and growth processes of atmospheric molecular clusters. In particular, they will develop and apply novel machine learning techniques based on quantum chemical calculations. The results will aid in understanding how aerosol particles are formed and will significantly improve our understanding of climate drivers at the molecular level.
Scientific challenges and impact to society
The application of machine learning techniques for studying the formation and growth of atmospheric clusters pose a significant scientific challenge. According to Jonas Elm, the research team will need to develop and test several machine learning techniques to identify which method is best at representing the quantum chemical data and has the most efficient learning curves.
The project will give direct information into how atmospheric particles are formed and especially which chemical compounds contribute to the process. Jonas and his team will be able to identify chemical species which efficiently form particles and hence should be regulated. The results are going to be implemented in an atmospheric model which allows the researchers to significantly reduce the uncertainty in 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 the researchers a significantly improved understanding of our current and future climate.