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Title
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Machine learning approach to constructing tight binding models for solids with application to BiTeCl
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Journal of applied physics / American Institute of Physics. - New York, N.Y., 1937, currens
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Publication
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New York, N.Y.
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American Institute of Physics
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2020
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ISSN
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0021-8979
[print]
1089-7550
[online]
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DOI
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10.1063/5.0023980
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Volume/pages
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128
:21
(2020)
, 9 p.
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Article Reference
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215107
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ISI
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000597311900001
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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