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SUMMARY:Solvated electron from first principles and machine learning - Dr 
 Jinggang Lan\,  École Polytechnique Fédérale de Lausanne
DTSTART:20230515T133000Z
DTEND:20230515T140000Z
UID:TALK201010@talks.cam.ac.uk
CONTACT:Dr Venkat Kapil
DESCRIPTION:The nature of the bulk hydrated electron has been a challenge 
 for both experiment and theory due to its short lifetime and high reactivi
 ty\, and the need for a high-level of electronic structure theory to achie
 ve predictive accuracy. The lack of a classical atomistic structural formu
 la makes it exceedingly difficult to model the solvated electron using con
 ventional empirical force fields\, which describe the system in terms of i
 nteractions between point particles associated with atomic nuclei. Here we
  overcome this problem using a machine-learning model\, that is sufficient
 ly flexible to describe the effect of the excess electron on the structure
  of the surrounding water\, without including the electron in the model ex
 plicitly. The resulting potential is not only able to reproduce the stable
  cavity structure but also recovers the correct localization dynamics that
  follow the injection of an electron in neat water. The machine learning m
 odel achieves the accuracy of the state-of-the-art correlated wave functio
 n method it is trained on. It is sufficiently inexpensive to afford a full
  quantum statistical and dynamical description and allows us to achieve ac
 curate determination of the structure\, diffusion mechanisms\, vibrational
  spectroscopy and temperature-dependent properties of the solvated electro
 n.
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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