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SUMMARY:Inverse Design of Simple Liquids using Machine Learning and the Or
 nstein-Zernike Equation - Rhys Goodall
DTSTART:20200414T153000Z
DTEND:20200414T160000Z
UID:TALK141502@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:The Ornstein-Zernike framework provides an elegant route for s
 olving the inverse problem of determining a pairwise interaction potential
  for a simple liquid given its structure. However\, in order to realise th
 e potential of the formalism superior closure relationships are required. 
 Current approximate closure relationships have been shown to have restrict
 ed universality and give rise to thermodynamic inconsistencies. In this wo
 rk rather than attempting to analytically derive a new closure relationshi
 p we return to the point of the approximation and investigate whether mach
 ine learning can be used to infer a universal closure for the framework di
 rectly from simulation data. We show that this is a fruitful approach that
  allows for improved inversion performance.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
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