The DOI matches its Crossref record.
Predicting Band Gaps with Hybrid Density Functionals
Open accessAbstract
We compare the ability of four popular hybrid density functionals (B3LYP, B3PW91, HSE, and PBE0) for predicting band gaps of semiconductors and insulators over a large benchmark set using a consistent methodology. We observe no significant statistical difference in their overall performance, although the screened hybrid HSE is more accurate for typical semiconductors. HSE can improve its accuracy for large band gap materials-without affecting that of semiconductors-by including a larger portion of Hartree-Fock exchange in its short-range. Given that screened hybrids are computationally much less expensive than their global counterparts, we conclude that they are a better option for the black box prediction of band gaps.
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
Cited by
WIEN2k: An APW+lo program for calculating the properties of solids
Blaha, P., Schwarz, K., Tran, F., Laskowski, R., Madsen, G. K. H., & Marks, L. D. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5143061 · Cited by 2,696
Understanding band gaps of solids in generalized Kohn–Sham theory
Perdew, J. P., Yang, W., Burke, K., Yang, Z., Gross, E. K. U., Scheffler, M., Scuseria, G. E., Henderson, T. M., Zhang, I. Y., Ruzsinszky, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621352114 · Cited by 616
Self-citationPredicting the Band Gaps of Inorganic Solids by Machine Learning
Zhuo, Y., Mansouri Tehrani, A., & Brgoch, J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00124 · Cited by 455
Borlido, P., Schmidt, J., Huran, A. W., Tran, F., Marques, M. A. L., & Botti, S. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00360-0 · Cited by 341
Electronic Structure: Basic Theory and Practical Methods
Martin, R. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108555586 · Cited by 269
Metadata
- Authors
- Garza, A. J., & Scuseria, G. E.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-10-07
- Volume
- 7
- Issue
- 20
- Pages
- 4165-4170
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2526397171
- Cited by
- 547
- References
- 0
- FWCI
- 20.32
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Predicting Band Gaps with Hybrid Density Functionals. https://app.cochat.ai/papers/10.1021/acs.jpclett.6b01807
- The paper
- Garza, A. J., & Scuseria, G. E. (2016). Predicting Band Gaps with Hybrid Density Functionals. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/acs.jpclett.6b01807
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.
