Machine-learned model Hamiltonian and strength of spin-orbit interaction in strained Mg2X (X = Si, Ge, Sn, Pb)
Abstract
Machine-learned multi-orbital tight-binding (MMTB) Hamiltonian models have been developed to describe the electronic characteristics of intermetallic compounds , and subject to strain. The MMTB models incorporate spin-orbital mediated interactions and they are calibrated to the electronic band structures calculated via density functional theory (DFT) by a massively parallelized multi-dimensional Monte-Carlo search algorithm. The results show that a machine-learned five-band tight-binding model reproduces the key aspects of the valence band structures in the entire Brillouin zone. The five-band model reveals that compressive strain localizes the contribution of the orbital of to the conduction bands and the outer shell orbitals of to the valence bands. In contrast, tensile strain has a reversed effect as it weakens the contribution of the orbital of and the outer shell orbitals of to the conduction bands and valence bands, respectively. The bonding in the compounds is negligible compared to the bonding components, which follow the hierarchy , and the largest variation against strain belongs to . The five-band model allows for estimating the strength of spin-orbit coupling (SOC) in and obtaining its dependence on the atomic number of and strain. Further, the band structure calculations demonstrate a significant band gap tuning and band splitting due to strain. A compressive strain of can open a band gap at the point in metallic , whereas a tensile strain of closes the semiconducting band gap of . A tensile strain of removes the three-fold degeneracy of valence bands at the point in semiconducting .
Keywords
Cite
@article{arxiv.2203.01353,
title = {Machine-learned model Hamiltonian and strength of spin-orbit interaction in strained Mg2X (X = Si, Ge, Sn, Pb)},
author = {Mohammad Alidoust and Erling Rothmund and Jaakko Akola},
journal= {arXiv preprint arXiv:2203.01353},
year = {2022}
}
Comments
16 pages, 7 figures, 4 tables, Accepted for publication