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

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Start:Mittwoch, 22 Mai 2019Zeit:16:30
Ende:Mittwoch, 22 Mai 2019Zeit:18:00
Kategorie: Uni Basel, Physikalische Chemie

Excited-state Dynamics Simulations aliquip with Machine Learning

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

Ort: Physikalische Chemie, Departement Chemie, Universität Basel, Kleiner Hörsaal, Raum 4.04, 2. Stock
Klingelbergstrasse 80, 4056, Basel
Email: Senden
Webseite: http://www.chemie.unibas.ch
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Mariella Schneiter (Ersteller)