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SUMMARY:  Neural DFT: A transformative approach to multiscale modelling - 
 Dr Stephen J Cox (Durham University)
DTSTART:20250317T143000Z
DTEND:20250317T150000Z
UID:TALK227005@talks.cam.ac.uk
CONTACT:Alexander R Epstein
DESCRIPTION:Many problems across biology\, chemistry\, physics\, and mater
 ials science are inherently multiscale in nature. In this talk\, I will di
 scuss how classical density functional techniques\, combined with deep lea
 rning methods\, offer new and exciting ways to probe emergent physics acro
 ss length scales. Not only can we now understand structure and thermodynam
 ics accurately and efficiently\, and across much larger length scales than
  with molecular simulations\, but we can also investigate new physics. For
  example\, I will introduce “dielectrocapillarity” --- that is\, how e
 lectric field gradients impact phase behavior and criticality of fluids.\n
 \n \n\n    AT Bui & SJ Cox\, Learning classical density functionals for io
 nic fluids\, arXiv.2410.02556 (2024) [accepted in Phys. Rev. Lett.]\n    A
 T Bui & SJ Cox\, Dielectrocapillarity for exquisite control of fluids\, ar
 Xiv.2503.09855 (2025)\n    AT Bui & SJ Cox\, A first principles approach t
 o electromechanics in liquids\, arXiv.2503.09768 (2025)\n    Sammüller et
  al.\, Neural functional theory for inhomogeneous fluids: Fundamentals and
  applications\, Proc. Natl. Acad. Sci. USA\, 120\, e2312484120 (2023)\n   
  Sammüller et al.\, Hyperdensity Functional Theory of Soft Matter\, Phys.
  Rev. Lett. 133\, 098201 (2024)
LOCATION:https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHptUXlRSkppQT0
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