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 sint with Machine Learning

Light dolor can induce a sint wealth of processes dolor in electronically excited pariatur. states but corresponding nulla simulations are limited qui by the costly non computations of potential aliquip energy surfaces. A in solution to this deserunt problem will be Lorem presented, where machine minim learning potentials are sunt used to carry reprehenderit out excited-state molecular ullamco dynamics. The dynamics ipsum is simulated with Ut our surface hopping velit approach called SHARC eiusmod (surface hopping including ullamco arbitrary couplings), which officia is able to sunt treat not only sint kinetic dynamical couplings dolor but also any Lorem other arbitrary coupling voluptate on an equal consectetur footing. Consequently, machine amet, learning is employed quis not only for fugiat potentials but also proident, for nonadiabatic couplings. ullamco These developments open ea up the possibility exercitation to simulate time do scales in the ut nanosecond regime compared liqua. to a few anim picoseconds in conventional velit 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
This event is public. Anyone can attend and invite others to attend.
Mariella Schneiter (creator)