Machine-learned model Hamiltonian and strength of spin-orbit interaction in strained Mg2X (X = Si, Ge, Sn, Pb)
Mohammad Alidoust, Erling Rothmund, and Jaakko Akola

TL;DR
This study develops machine-learned tight-binding models for Mg2X compounds under strain, revealing how strain and atomic number influence electronic structure, spin-orbit coupling, and band gap tuning.
Contribution
The paper introduces a machine-learned five-band tight-binding model calibrated to DFT data, capturing strain effects and spin-orbit interactions in Mg2X compounds.
Findings
Strain significantly alters band structures and orbital contributions.
Compressing Mg2Pb opens a band gap at the Gamma point.
Tensile strain closes the band gap in Mg2Si.
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…
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