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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 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 (up) Additional Links UA library record; WoS full record; WoS citing articles  
  Impact Factor 3.2 Times cited 2 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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