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SUMMARY:Unsupervised Machine Learning and Band Topology - Robert-Jan Slage
 r
DTSTART:20201012T160000Z
DTEND:20201012T163000Z
UID:TALK151930@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:The study of topological band structures is an active area of 
 research in condensed matter physics and beyond. In this talk I present so
 me recent progress in this field upon combining them with developments in 
 machine learning. Specifically\, I introduce an unsupervised machine learn
 ing approach that searches for and retrieves paths of adiabatic deformatio
 ns between Hamiltonians\, thereby clustering them according to their topol
 ogical properties. The algorithm is general\, as it does not rely on a spe
 cific parametrization of the Hamiltonian and is readily applicable to any 
 symmetry class. We demonstrate the approach using several different models
  in both one and two spatial dimensions and for different symmetry classes
  with and without crystalline symmetries. Accordingly\, it is also shown h
 ow trivial and topological phases can be diagnosed upon comparing with a g
 enerally designated set of trivial atomic insulators.\n
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, https://zoom.us/j/2635916
 003
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