Data · dataset · 2026
The data of the article “Tight-Binding Model and Optical Conductivity Data for the Kekulé Honeycomb Lattice”
Listed in ScienceDB
This dataset consists of numerical simulation programs for the tight-binding model of the Kekulé honeycomb lattice and its optical conductivity response.
Description
The data are generated by Python programs, mainly based on the pybinding framework to construct the O-Kekulé honeycomb lattice model, and combined with NumPy, Matplotlib, tqdm, numba, and other tools to complete Hamiltonian construction, eigenvalue solving, velocity operator calculation, optical conductivity summation, and post-processing plotting.
In the model, two types of nearest-neighbor hopping parameters, t1 and t2, are adopted, where t1 is used as the energy unit, and finite-size samples are obtained by cutting a hexagonal region. During the calculation, a Kekulé honeycomb lattice containing six sublattices is first defined. Then, the finite-size Hamiltonian matrix is constructed, and numerical diagonalization is used to obtain the eigenenergies and eigenvectors.
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The velocity operators Vx and Vy are calculated from the commutation relation between the position operator and the Hamiltonian. On this basis, according to the Kubo formula, the longitudinal optical conductivity sigma_xx under different energies is numerically summed. Furthermore, the eigenstates participating in the transitions are classified into corner states, edge states, and bulk states, which are used to analyze the contributions of transitions between different states to the total optical conductivity.The variable in the data is the frequency in the optical response, namely the dimensionless energy h_bar ω/t1.
For the optical conductivity calculation, the basic scanning step is usually 0.005. In some calculations, parameters such as the chemical potential Mu, temperature kBT, broadening factor Gamma, and cutting size are also varied to compare the presence or absence of topological corner states and the effects of different physical parameters on the optical conductivity.The data files are mainly saved in .txt text format and .eps image format. h_omega.txt records the energy sampling points and can be used as the horizontal coordinate of the optical conductivity data; total_sigma_xx.txt records the total longitudinal optical conductivity sigma_xx; B-B_sigma_xx.txt, B-C_sigma_xx.txt, B-E_sigma_xx.txt, E-E_sigma_xx.txt, E-C_sigma_xx.txt, and C-C_sigma_xx.txt respectively record the contributions of bulk state–bulk state, bulk state–corner state, bulk state–edge state, edge state–edge state, edge state–corner state, and corner state–corner state transitions to the optical conductivity.
The above optical conductivity data correspond line by line to the energy sampling points in h_omega.txt, that is, the optical conductivity value in the i-th line corresponds to the h_omega/t1 value in the i-th line. If the data are saved in complex form in the file, the real part is usually used to plot the Re(sigma_xx) optical conductivity. Vx.txt and Vy.txt respectively save the velocity operator matrices calculated from the Hamiltonian and position operators; model.eps saves the schematic diagram of the finite-size lattice structure; energy_band_1.eps and energy_band_2.eps save the energy spectrum calculation results.
The result files are usually stored in the result folder according to the calculation parameters, and the folder names contain information such as sample size, chemical potential, temperature, and broadening factor, which facilitates distinguishing different parameter groups.The data files are saved in common text and vector image formats, and can be read using numpy.loadtxt or numpy.genfromtxt in Python, or imported into MATLAB, Origin, and other software for subsequent plotting and analysis.
The .eps image files can be opened using Matplotlib, Inkscape, Adobe Illustrator, or vector graphics software that supports the EPS format. If the data need to be regenerated, it is recommended to use Python 3.8.5 and the corresponding version of the pybinding environment, and to install dependency libraries such as NumPy, Matplotlib, tqdm, and numba.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00213.00260 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Image 75% · Longitudinal study 65% · Nanotechnology 71% · Simulation 75% · Text 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00213.00260 | 9 d ago | JSON v1 |
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|---|---|---|---|
| concepts[field].anzsrc:group:4018 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
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| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
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| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:longitudinal-study | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:text | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |