Improving thermodynamic property estimates of SOA constituents using machine learning


Main funder

Funder's project number338171


Funds granted by main funder (€)

  • 214 976,00


Funding program


Project timetable

Project start date01/09/2021

Project end date31/08/2024


Summary

Low volatility organic compounds contribute to the formation and growth of atmospheric aerosol particles. Thermodynamic properties of these compounds affect the properties of the aerosol, especially their lifetimes and ability to act as cloud condensation nuclei. This project investigates the potential of using machine learning based methods property calculations. Machine learning is used in combination with quantum chemistry to compute thermodynamic properties of relevant multifunctional organic compounds in various atmospherically relevant systems. The computational results are compared with corresponding experimental values.


Principal Investigator


Other persons related to this project (JYU)


Primary responsible unit


Follow-up groups

Profiling areaNanoscience Center (Department of Physics PHYS, JYFL) (Faculty of Mathematics and Science) (Department of Chemistry CHEM) (Department of Biological and Environmental Science BIOENV) NSC


Related publications and other outputs


Related research datasets


Last updated on 2024-09-07 at 09:08