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Author Nakhaee, M.; Ketabi, S.A.; Peeters, F.M. doi  openurl
  Title Machine learning approach to constructing tight binding models for solids with application to BiTeCl Type A1 Journal article
  Year (down) 2020 Publication Journal Of Applied Physics Abbreviated Journal J Appl Phys  
  Volume 128 Issue 21 Pages 215107  
  Keywords A1 Journal article; Condensed Matter Theory (CMT)  
  Abstract Finding a tight-binding (TB) model for a desired solid is always a challenge that is of great interest when, e.g., studying transport properties. A method is proposed to construct TB models for solids using machine learning (ML) techniques. The approach is based on the LCAO method in combination with Slater-Koster (SK) integrals, which are used to obtain optimal SK parameters. The lattice constant is used to generate training examples to construct a linear ML model. We successfully used this method to find a TB model for BiTeCl, where spin-orbit coupling plays an essential role in its topological behavior.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 000597311900001 Publication Date 2020-12-03  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0021-8979; 1089-7550 ISBN Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor 3.2 Times cited 10 Open Access  
  Notes ; This work was supported by the Methusalem program of the Flemish government and was partially supported by BOF (UAntwerpen Grant Reference No. ADPERS/BAP/RS/ 2019). We would like to thank one of the anonymous referees for assisting us in making the paper more accessible to the reader. ; Approved Most recent IF: 3.2; 2020 IF: 2.068  
  Call Number UA @ admin @ c:irua:174380 Serial 6691  
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