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SUMMARY:Machine Learning in Chemical Reaction Space - Sina Stocker\, Techn
 ical University of Munich
DTSTART:20211115T160000Z
DTEND:20211115T163000Z
UID:TALK162328@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Reaction networks are essential tools for the analysis\, visua
 lization and understanding of chemical processes in such diverse fields as
  catalysis\, combustion and the origin of life. For complex processes\, th
 e number of individual reaction steps in such a network is so large\nthat 
 an exhaustive first-principles calculation of all reaction energies and ra
 tes becomes prohibitively expensive. In this talk\, we use machine learnin
 g (ML) to accelerate the exploration of chemical reaction space\, in analo
 gy to the more established ML-based exploration of chemical space. To this
  end\, we generated a new reactive reference database (Rad-6) of open- and
  closed-shell organic molecules. This allows us to apply "chemical space" 
 ML methods to predict the chermochemistry of reaction networks. The perfor
 mance of\nthese methods confirms the potential of ML for the high-throughp
 ut screening of large reaction networks.\n
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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