Bandgaps of long-period polytypes of IV, IV-IV, and III-V semiconductors estimated with an Ising-type additivity model

砷化物 布里渊区 材料科学 带隙 亚稳态 凝聚态物理 磷化物 伊辛模型 半导体 纤锌矿晶体结构 声子 电子能带结构 混合功能 结晶学 密度泛函理论 光电子学 物理 化学 砷化镓 计算化学 冶金 量子力学
作者
Raghunathan Ramakrishnan,S. K. Jain
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:159 (12) 被引量:2
标识
DOI:10.1063/5.0166149
摘要

We apply an Ising-type model to estimate the bandgaps of the polytypes of group IV elements (C, Si, and Ge) and binary compounds of groups: IV–IV (SiC, GeC, and GeSi), and III–V (nitride, phosphide, and arsenide of B, Al, and Ga). The models use reference bandgaps of the simplest polytypes comprising 2–6 bilayers calculated with the hybrid density functional approximation, HSE06. We report four models capable of estimating bandgaps of nine polytypes containing 7 and 8 bilayers with an average error of ≲0.05 eV. We apply the best model with an error of <0.04 eV to predict the bandgaps of 497 polytypes with up to 15 bilayers in the unit cell, providing a comprehensive view of the variation in the electronic structure with the degree of hexagonality of the crystal structure. Within our enumeration, we identify four rhombohedral polytypes of SiC—9R, 12R, 15R(1), and 15R(2)—and perform detailed stability and band structure analysis. Of these, 15R(1) that has not been experimentally characterized has the widest bandgap (>3.4 eV); phonon analysis and cohesive energy reveal 15R(1)-SiC to be metastable. Additionally, we model the energies of valence and conduction bands of the rhombohedral SiC phases at the high-symmetry points of the Brillouin zone and predict band structure characteristics around the Fermi level. The models presented in this study may aid in identifying polytypic phases suitable for various applications, such as the design of wide-gap materials, that are relevant to high-voltage applications. In particular, the method holds promise for forecasting electronic properties of long-period and ultra-long-period polytypes for which accurate first-principles modeling is computationally challenging.
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