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DTSTART:20220327T030000
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DTSTAMP:20260405T232535Z
UID:626113420143f313197347@ist.ac.at
DTSTART:20220509T140000
DTEND:20220509T160000
DESCRIPTION:Speaker: Frank Noe\nhosted by Bingqing Cheng\nAbstract: Molecul
 ar simulation may ideally serve as a "computational laboratory"\, with is 
 ability to observe both structure and dynamics at high resolution and to s
 imulate molecules that are difficult to synthesize. However\, it also suff
 ers from fundamental limitations\, in particular in the accurate modeling 
 of molecules and in the efficient computation of experimental observables.
  By leveraging the latest developments in machine learning\, we can advanc
 e molecular simulation algorithms to make significant progress at these fr
 onts without sacrificing rigorous physics.In this talk\, I will give an ov
 erview over our work on the highly accurate computation of quantum states 
 with deep fermionic neural networks and Quantum Monte Carlo\, and addressi
 ng the many-body sampling problem using deep Markov State Models and gener
 ative deep learning.
LOCATION:Raiffeisen Lecture Hall\, Central Building\, ISTA
ORGANIZER:kharppre@ist.ac.at
SUMMARY:Frank Noe: ELLIS Talk - Advancing molecular simulation with machine
  learning
URL:https://talks-calendar.ista.ac.at/events/3736
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