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CATEGORIES:Lennard-Jones Centre
SUMMARY:Specific ion effects in aqueous electrolyte soluti
 ons from first-principles derived neural network p
 otentials  - Dr Shuwen Yue\, Massachusetts Institu
 te of Technology\, USA
DTSTART;TZID=Europe/London:20221017T143000
DTEND;TZID=Europe/London:20221017T150000
UID:TALK184001AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/184001
DESCRIPTION:We investigate specific ion effects on the structu
 re and dynamics of water in aqueous electrolyte so
 lutions using neural network potentials trained on
  DFT predictions using the SCAN functional. Ion sp
 ecificity of bulk electrolyte solutions following 
 the Hofmeister series can induce wide-ranging effe
 cts on the dynamics of water - kosmotropic ions mi
 tigate water mobility and chaotropic ions accelera
 te water molecules. Many existing studies of this 
 phenomena apply conventional empirical models whic
 h are inherently limited in transferability and ac
 curacy across a range of concentrations. In this w
 ork\, in order to explore ion solvation characteri
 stics with a first principles level of accuracy\, 
 we train neural networks to learn highly complex a
 nd multi-dimensional interactions native to DFT re
 presentations. We find that these potentials overc
 ome the limitations of conventional empirical forc
 e fields in representing water dynamics with conce
 ntration dependence. We then use the potentials to
  probe the underlying mechanisms of ion-induced wa
 ter structure and mobility for a series of alkali 
 halide ions (KCl\, CsCl\, NaBr\, NaCl) in bulk sol
 ution.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pY
 SHptUXlRSkppQT09
CONTACT:Dr Venkat Kapil
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