BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Bosonic Quantum Solvation Enabled by Machine Learning - Dominik Ma
 rx (Ruhr-Universität Bochum)
DTSTART:20240222T140000Z
DTEND:20240222T150000Z
UID:TALK207598@talks.cam.ac.uk
CONTACT:Bo Peng
DESCRIPTION:My talk will focus on our recent advances that allow us to per
 form highly accurate and converged path integral simulations of flexible m
 olecules including their reactions in bosonic solvents at 1 Kelvin or less
 . Our approach is based on using machine learning potentials to describe t
 he many-body interactions at the level of coupled cluster electronic struc
 ture theory. Selected applications will be used to explore to what extent 
 Bose-Einstein statistics of the liquid environment such as manifestations 
 of local superfluidity or supersolidity are critical to understand phenome
 na as probed by the embedded molecular species.
LOCATION:TCM Seminar Room
END:VEVENT
END:VCALENDAR