BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Representing potential energy surfaces with neural networks - Matt
 i Hellström\, SCM\, Amsterdam
DTSTART:20200608T153000Z
DTEND:20200608T160000Z
UID:TALK142624@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Neural networks can efficiently calculate atomistic potential 
 energy surfaces\, allowing for large-scale molecular dynamics simulations.
 \n\nIn this talk I'll describe how so-called high-dimensional neural netwo
 rk potentials\, as proposed by Behler and Parrinello [1]\, can be construc
 ted and fitted to reproduce ab-initio or density functional theory results
 . I'll also demonstrate some recent applications of this approach to nucle
 ar quantum effects in electrolyte solutions [2] and anisotropic proton dif
 fusion at solid-liquid interfaces [3].\n\nMoreover\, I'll briefly describe
  another type of neural network potential based on a graph convolutions\, 
 with a particular emphasis on "PiNet"\, which we recently demonstrated cou
 ld predict a wide range of properties for molecules\, liquids\, and materi
 als [4].\n\n[1]. J. Behler\, M. Parrinello. Phys. Rev. Lett. 98\, 146401\n
 [2]. M. Hellstrom\, M. Ceriotti\, J. Behler. J. Phys. Chem. B 2018\, 122\,
  44\, 10158–10171\n[3]. M. Hellstrom\, V. Quaranta\, J. Behler. Chem. Sc
 i.\, 2019\, 10\, 1232-1243\n[4]. Y. Shao\, M. Hellstrom\, P.D. Mitev\, L. 
 Knijff\, C. Zhang. J. Chem. Inf. Model. 2020\, 60\, 3\, 1184–1193
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
END:VEVENT
END:VCALENDAR