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| Author | Nakhaee, M.; Ketabi, S.A.; Peeters, F.M. | ||||
| 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. | ||||
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| Language | Wos | 000597311900001 | Publication Date | 2020-12-03 | |
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| 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 | 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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