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 nisi with Machine Learning

Light dolor can induce a sit wealth of processes tempor in electronically excited aliquip states but corresponding anim simulations are limited id by the costly sunt computations of potential pariatur. energy surfaces. A occaecat solution to this laboris problem will be sint presented, where machine Ut learning potentials are veniam, used to carry labore out excited-state molecular dolor dynamics. The dynamics dolor is simulated with occaecat our surface hopping Lorem approach called SHARC amet, (surface hopping including sint arbitrary couplings), which in is able to consectetur treat not only velit kinetic dynamical couplings labore but also any fugiat other arbitrary coupling sint on an equal in footing. Consequently, machine irure learning is employed occaecat not only for elit, potentials but also laborum. for nonadiabatic couplings. proident, These developments open tempor up the possibility do to simulate time liqua. scales in the adipisicing nanosecond regime compared consectetur to a few elit, picoseconds in conventional proident, 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
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Mariella Schneiter (creator)