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SUMMARY:Infrared Spectra at Coupled Cluster Accuracy from Neural Network R
 epresentation  - Fabien Brieuc\, CEA
DTSTART:20211122T163000Z
DTEND:20211122T170000Z
UID:TALK162337@talks.cam.ac.uk
CONTACT:Dr Christoph Schran
DESCRIPTION:Infrared spectroscopy is one of the most useful experimental t
 echniques to identify and study molecular systems. However\, the analysis 
 and interpretation of those spectra can be very challenging for complex sy
 stems. In these cases theoretical spectroscopy based on molecular dynamics
  (MD) simulations can help unravel the microscopic details of the observed
  spectra. These simulations however require an accurate description of the
  electronic structure of the system to give reliable results. For many sys
 tems\, the highest accuracy is currently provided by Coupled Cluster (CC) 
 theory\, sometimes refer to as the current “gold standard” of theoreti
 cal chemistry. However\, the high computational cost of CC methods prohibi
 t their use on-the-fly in MD simulation. Fortunately\, Machine Learning (M
 L) methods have been actively developed in the last decade with great succ
 ess to represent in particular the potential energy surface\, as well as o
 ther properties\, of chemical systems. In this talk we will show how one o
 f these ML approaches\, namely high-dimensional neural networks\, can be u
 sed in order to describe both the PES and the electric dipole moments of m
 olecular systems in order to run MD simulations essentially at CC level an
 d obtain highly accurate IR spectra.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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