The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ExtractingEversusk⃗effective band structure from supercell calculations on alloys and impurities
Abstract
The supercell approach to defects and alloys has circumvented the limitations of those methods that insist on using artificially high symmetry, yet this step usually comes at the cost of abandoning the language of $E$ versus $\stackrel{P\vec}{k}$ band dispersion. Here we describe a computational method that maps the energy eigenvalues obtained from large supercell calculations into an effective band structure (EBS) and recovers an approximate $E(\stackrel{P\vec}{k})$ for alloys. Making use of supercells allows one to model a random alloy A${}_{1\ensuremath{-}x}$B${}_{x}$C by occupying the sites A and B via a coin-toss procedure, affording many different local environments (polymorphic description) to occur. We present the formalism and implementation details of the method and apply it to study the evolution of the impurity band appearing in the dilute GaN:P alloy. We go beyond the perfectly random case, realizing that many alloys may have nonrandom microstructures, and investigate how their formation is reflected in the EBS. It turns out that the EBS is extremely sensitive in determining the critical disorder level for which delocalized states start to appear in the intermediate band. In addition, the EBS allows us to identify the role played by atomic relaxation in the positioning of the impurity levels.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Absence of Diffusion in Certain Random Lattices
Anderson, P. W. (1958) · Physical Review · doi:10.1103/physrev.109.1492 · Cited by 12,468
Cited by
Recent developments in the ABINIT software package
Gonze, X., Jollet, F., Abreu Araujo, F., Adams, D., Amadon, B., Applencourt, T., Audouze, C., Beuken, J.-M., Bieder, J., Bokhanchuk, A., et al. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2016.04.003 · Cited by 895
Zheng, Q., Chu, W., Zhao, C., Zhang, L., Guo, H., Wang, Y., Jiang, X., & Zhao, J. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1411 · Cited by 452
Metadata
- Authors
- Popescu, V., & Zunger, A.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2012-02-02
- Volume
- 85
- Issue
- 8
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2024367821
- Cited by
- 495
- References
- 1
- FWCI
- 9.56
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.