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SUMMARY:Light-matter interactions: from ab initio molecular dynamics and m
 achine learning to x-ray spectroscopy - Dr Morgane Vacher\, University of 
 Nantes
DTSTART:20210519T133000Z
DTEND:20210519T143000Z
UID:TALK157027@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Computer simulations are a key complement to experiments in th
 e laboratory\, providing much greater details of a molecular process than 
 can be observed experimentally. For instance\, ab initio molecular dynamic
 s simulations are often key to the understanding of the mechanism\, rate a
 nd yield of chemical reactions [1\,2\,3]. One current challenge is the in-
 depth analysis of the large amount of data produced by the simulations\, i
 n order to produce valuable insight and general trends. In the first part 
 of my talk\, I will present recent machine learning analysis tools used to
  extract relevant information from ab initio molecular dynamics simulation
 s without a priori knowledge on chemical reactions [4\,5]. It is demonstra
 ted that\, in order to make accurate predictions\, the models evidence emp
 irical rules that are\, today\, part of the common chemical knowledge. Thi
 s opens the way for conceptual breakthroughs in chemistry where machine an
 alysis would provide a source of inspiration to humans. In the second part
  of my talk\, I will show recent experimental and theoretical results on t
 he photo-induced dynamics of an iron photosensitizer. Coherent structural 
 dynamics in the excited state of an iron photosensitizer was observed thro
 ugh oscillations in the intensity of Kalpha x-ray emission spectroscopy (X
 ES). Using multiconfigurational wavefunction calculations\, we explain the
  origin of the unexpected sensitivity of core-to-core transitions to struc
 tural dynamics [6\,7].\n\n\n\nReferences\n\n[1] M. Vacher\, P. Farahani\, 
 A. Valentini\, L. M. Frutos\, H. O. Karlsson\, I. Fdez. Galván and R. Lin
 dh\, J. Phys. Chem. Lett. 8\, 3790-3794 (2019).\n[2] O. Schalk\, J. Galian
 a\, T. Geng\, T. L. Larsson\, R. D. Thomas\, I. Fdez. Galván\, T. Hansson
  and M. Vacher\, J. Chem. Phys. 152\, 064301 (2020).\n[3] J. Norell\, M. O
 delius and M. Vacher\, Struct. Dyn. 7\, 024104 (2020).\n[4] F. Häse\, I. 
 Fdez. Galván\, A. Aspuru-Guzik\, R. Lindh and M. Vacher\, Chem. Science 1
 0\, 2298-2307 (2019).\n[5] F. Häse\, I. Fdez. Galván\, A. Aspuru-Guzik\,
  R. Lindh and M. Vacher\, J. Phys.: Conf. Ser. 1412\, 042003 (2020).\n[6] 
 K. Kunnus\, M. Vacher\, et al\, Nature Comm. 11\, 634 (2020).\n[7] M. Vach
 er\, K. Kunnus\, M. G. Delcey\, K. J. Gaffney and M. Lundberg\, Struct. Dy
 n. 7\, 044102 (2020).\n
LOCATION:Zoom: Meeting ID: 919 4908 5468 Passcode: 711578
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