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SUMMARY:Representation Learning from Stoichiometry - Rhys Goodall
DTSTART:20200203T163000Z
DTEND:20200203T170000Z
UID:TALK138784@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:Much has been said about the ability of machine learning to re
 duce the computational cost of quantum mechanical calculations - by closel
 y approximating the approximation level of reference data (within a domain
  of applicability close to the data manifold). In order to obtain the high
 est possible accuracy the SOTA prediction methods are all structure based 
 - SOAP\, SchNet (DTNN)\, MBTR\, CGCNN/MegNet.\n\nHowever for applications 
 within materials discovery we often start without knowledge of the crystal
  structure and so new approaches are needed if we want to use machine lear
 ning to accelerate such workflows.\n\nI will briefly summarise what has be
 en done in this structure-free domain and then introduce a new end-to-end 
 model that addresses some of the short comings. https://arxiv.org/abs/1910
 .00617
LOCATION:Mott Seminar (531) room\, top floor of the Mott Building\, in the
  Cavendish Laboratory\, West Cambridge.
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