The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A high-throughput infrastructure for density functional theory calculations
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Citations
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Projector augmented-wave method
Blöchl, P. E. (1994) · Physical Review B · doi:10.1103/physrevb.50.17953 · Cited by 92,732
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Special points for Brillouin-zone integrations
Monkhorst, H. J., & Pack, J. D. (1976) · Physical Review B · doi:10.1103/physrevb.13.5188 · Cited by 71,721
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
A new mixing of Hartree–Fock and local density-functional theories
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464304 · Cited by 16,515
A relational model of data for large shared data banks
Codd, E. F. (1970) · Communications of the ACM · doi:10.1145/362384.362685 · Cited by 5,547
The entity-relationship model—toward a unified view of data
Chen, P. P.-S. (1976) · ACM Transactions on Database Systems · doi:10.1145/320434.320440 · Cited by 4,708
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
Crystal structure prediction using ab initio evolutionary techniques: Principles and applications
Oganov, A. R., & Glass, C. W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2210932 · Cited by 2,596
High-throughput electronic band structure calculations: Challenges and tools
Setyawan, W., & Curtarolo, S. (2010) · Computational Materials Science · doi:10.1016/j.commatsci.2010.05.010 · Cited by 1,692
Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials · doi:10.1021/cm100795d · Cited by 580
Self-citationScott, D. J., Manos, S., & Coveney, P. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700269r · Cited by 20
Cited by
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
Self-citationXie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., & Wolverton, C. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,584
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
Metadata
- Authors
- Jain, A., Hautier, G., Moore, C. J., Ping Ong, S., Fischer, C. C., Mueller, T., Persson, K. A., & Ceder, G.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2011-06
- Volume
- 50
- Issue
- 8
- Pages
- 2295-2310
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2162653998
- Cited by
- 1,041
- References
- 14
- FWCI
- 20.45
- 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.
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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.
