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SEQUENCE:1
UID:UW-Physics-Event-5932
DTSTART:20200812T160000Z
DTEND:20200812T170000Z
DTSTAMP:20260415T005516Z
LAST-MODIFIED:20200714T135533Z
LOCATION:Please register for this online event: http://physicsmeetsml.
 org
SUMMARY:Discovering new phases of matter with unsupervised and interpr
 etable support vector machines \, Physics Meets ML\, Lode Pollet\,  LM
 U Munich
DESCRIPTION:I present the Tensorial Kernel Support Vector Machine (TK-
 SVM) as a tool to automate the classification of complicated phase dia
 grams for classical systems\, which is a complicated task when multipl
 e phases coexist and orders compete\, as is frequently the case in fru
 strated magnetism. The key property is the interpretability of the dec
 ision function\, from which the physical local order parameter can be 
 deduced irrespective of its rank. Furthermore\, we discuss a second in
 trinsic parameter of TK-SVM\, the bias\, which can be given a distinct
  physical meaning and which allows one to make an unsupervised graph a
 nalysis of the topology of the phase diagram. We illustrate our tool f
 or the classical XXZ model on the frustrated pyrochlore lattice. Unexp
 ectedly\, TK-SVM could also learn local constraints hinting at various
  types of spin liquids resulting in a complete classification of all t
 ypes of behavior for this model. TK-SVM was subsequently applied to th
 e Kitaev materials where we found a new type of magnetic order as well
  as new explicit formula’s for the local constraints of certain spin
  liquids\, proving the usefulness of TK-SVM in going beyond the state 
 of the art.<br>
\n<br>
\nReferences:<br>
\n<br>
\n    Phys. Rev. B 99\
 , 060404 (2019)<br>
\n    Phys. Rev. B 99\, 104410 (2019)<br>
\n    Ph
 ys. Rev. B 100\, 174408 (2019)<br>
\n    preprint arXiv:2004.14415 (20
 20)<br>
\n
URL:https://www.physics.wisc.edu/events/?id=5932
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