Machine learning approach to constructing tight binding models for solids with application to BiTeCl

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.

Publication year:2020

Keywords:A1 Journal article

BOF-keylabel:yes

BOF-publication weight:1

CSS-citation score:1

Authors:International

Authors from:Higher Education

Accessibility:Closed