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SUMMARY:"AI in chemistry and beyond: Highlights in the field" seminar "Dee
 p learning for photochemical reaction discovery and targeted molecular des
 ign" by Julia Westermayr
DTSTART:20221206T151500
DTEND:20221206T161500
DTSTAMP:20260920T161029Z
UID:708d3aaa07d7d9462804e177f650ddec37fac7917d27a37ef9d41c48
CATEGORIES:Conferences - Seminars
DESCRIPTION:Julia Westermayr is an Assistant Professor at the University o
 f Leipzig.\n\nPhotochemical simulations are powerful tools to study the li
 ght-induced breaking and formation of bonds in molecules and materials and
  can provide a deeper understanding of the underlying mechanisms that are 
 also crucial to deliver sustainable photocatalysis and efficient energy ma
 terials. However\, existing limitations of conventional techniques\, such 
 as high computational costs and low transferability to systems with more t
 han a few dozen atoms\, mean that we currently cannot address the most int
 eresting problems in materials science\, photochemistry\, and biology [1].
   \nIn this talk\, I will show how artificial intelligence can be used t
 o overcome the current limitations of photochemical simulations. I will de
 monstrate that photodynamics can be sped up by machine learning to allow s
 imulations on experimentally relevant time scales [2\,3] and high-throughp
 ut screening for the property-driven design of novel molecules [4\,5]. \n
  \n[1] J. Westermayr\, P. Marquetand Chem. Rev. 121(16)\, 9873-9926 (2021
 ). \n[2] J. Westermayr\, M. Gastegger\, M. Menger\, S. Mai\, L. González
 \, P. Marquetand Chem. Sci. 10\, 8100-8107 (2019). \n[3] J. Westermayr\, 
 M. Gastegger\, D. Vörös\, L. Panzenböck\, F. Jörg\, L. González\, P. 
 Marquetand\, Nat. Chem.\, 14\, 914-919 (2022).\n[4] J. Westermayr and R. J
 . Maurer\, Chem. Sci. 12\, 10755-10764 (2021).  \n[5] J. Westermayr\, J.
  Gilkes\, R. Barrett\, R. J. Maurer\, arXiv:2207.01476 (2022).\n\n
LOCATION:https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYl
 NRdz09
STATUS:CONFIRMED
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