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SUMMARY:Specific ion effects in aqueous electrolyte solutions from first-p
 rinciples derived neural network potentials  - Dr Shuwen Yue\, Massachuset
 ts Institute of Technology\, USA
DTSTART:20221017T133000Z
DTEND:20221017T140000Z
UID:TALK184001@talks.cam.ac.uk
CONTACT:Dr Venkat Kapil
DESCRIPTION:We investigate specific ion effects on the structure and dynam
 ics of water in aqueous electrolyte solutions using neural network potenti
 als trained on DFT predictions using the SCAN functional. Ion specificity 
 of bulk electrolyte solutions following the Hofmeister series can induce w
 ide-ranging effects on the dynamics of water - kosmotropic ions mitigate w
 ater mobility and chaotropic ions accelerate water molecules. Many existin
 g studies of this phenomena apply conventional empirical models which are 
 inherently limited in transferability and accuracy across a range of conce
 ntrations. In this work\, in order to explore ion solvation characteristic
 s with a first principles level of accuracy\, we train neural networks to 
 learn highly complex and multi-dimensional interactions native to DFT repr
 esentations. We find that these potentials overcome the limitations of con
 ventional empirical force fields in representing water dynamics with conce
 ntration dependence. We then use the potentials to probe the underlying me
 chanisms of ion-induced water structure and mobility for a series of alkal
 i halide ions (KCl\, CsCl\, NaBr\, NaCl) in bulk solution.
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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