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SUMMARY:  Excited-state learning for longer time scales and the simulation
  of excited tyrosine - Julia Westermayr\, University of Warwick
DTSTART:20211025T150000Z
DTEND:20211025T153000Z
UID:TALK162304@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Photodynamic simulations are powerful tools to decipher the fu
 ndamental mechanisms of light-initiated reactions. However\, high computat
 ional costs underlying calculations of accurate ab initio potentials for e
 xcited states limit the applicability of this method to short time scales\
 , and the possible breaking and formation of bonds after light excitation 
 complicates the search for a suitable reference method [1]. \n\nIn this ta
 lk\, I will present our efforts to improve photodynamics simulations with 
 machine learning (ML) models that can describe energies and forces of diff
 erent spin multiplicities and couplings between them. Our method is termed
  the SchNarc approach [2] and provides efficient generation of training se
 ts\, as well as a phase-free training algorithm that allows fitting of exc
 ited state properties that are arbitrary with respect to their sign. The p
 ower of our method is demonstrated with long time scale photodynamics of t
 he methyleneimmonium cation [3] and on the complex photochemistry of the a
 mino acid tyrosine. The latter illustrates the expressive power of ML mode
 ls that learn from different levels of theory and allow the dynamics of sy
 stems that could not be treated with the required accuracy otherwise [4]. 
 \n\n\n[1] J. Westermayr\, P. Marquetand Chem. Rev. 121(16)\, 9873-9926 (20
 21). \n\n[2] J. Westermayr\, M. Gastegger\, P. Marquetand J. Phys. Chem. L
 ett. 11(10)\, 3828-3834 (2020).  \n\n[3] J. Westermayr\, M. Gastegger\, M.
  Menger\, S. Mai\, L. González\, P. Marquetand Chem. Sci. 10\, 8100-8107 
 (2019). \n\n[4] J. Westermayr\, M. Gastegger\, D. Vörös\, L. Panzenböck
 \, F. Jörg\, L. González\, P. Marquetand\, arXiv: 2108.04373 (2021).
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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