Dr. Philipp Marquetand, University of Vienna, Institute of Theoretical Chemistry, Vienna, Austria 2019-05-22 16:30:00

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Start:Wednesday, 22 May 2019Time:16:30
End:Wednesday, 22 May 2019Time:18:00
Category: Uni Basel, Physikalische Chemie

Excited-state Dynamics Simulations enim with Machine Learning

Light nulla can induce a magna a wealth of processes aliquip in electronically excited nulla states but corresponding nostrud simulations are limited in by the costly veniam, computations of potential amet, energy surfaces. A ullamco solution to this esse problem will be qui presented, where machine nisi learning potentials are adipisicing used to carry sint out excited-state molecular in dynamics. The dynamics nulla is simulated with sunt our surface hopping Ut approach called SHARC dolor (surface hopping including Excepteur arbitrary couplings), which consequat. is able to exercitation treat not only mollit kinetic dynamical couplings dolore but also any nulla other arbitrary coupling dolore on an equal dolor footing. Consequently, machine est learning is employed nulla not only for Ut potentials but also magna a for nonadiabatic couplings. ut These developments open aute up the possibility fugiat to simulate time eiusmod scales in the officia nanosecond regime compared irure to a few ea picoseconds in conventional est approaches.

Venue: Physikalische Chemie, Departement Chemie, Universität Basel, Kleiner Hörsaal, Raum 4.04, 2. Stock
Klingelbergstrasse 80, 4056, Basel
Email: Send
Website: http://www.chemie.unibas.ch
Event Type
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Mariella Schneiter (creator)