The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
The AFLOW standard for high-throughput materials science calculations
Open accessAbstract
The Automatic-Flow (AFLOW) standard for the high-throughput construction of materials science electronic structure databases is described. Electronic structure calculations of solid state materials depend on a large number of parameters which must be understood by researchers, and must be reported by originators to ensure reproducibility and enable collaborative database expansion. We therefore describe standard parameter values for k-point grid density, basis set plane wave kinetic energy cut-off, exchange–correlation functionals, pseudopotentials, DFT+U parameters, and convergence criteria used in AFLOW calculations.
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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
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 75,638
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
Ab initio molecular dynamics for liquid metals
Kresse, G., & Hafner, J. (1993) · Physical Review B · doi:10.1103/physrevb.47.558 · Cited by 45,727
QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G. L., Cococcioni, M., Dabo, I., et al. (2009) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/21/39/395502 · Cited by 29,281
Soft self-consistent pseudopotentials in a generalized eigenvalue formalism
Vanderbilt, D. (1990) · Physical Review B · doi:10.1103/physrevb.41.7892 · Cited by 23,185
Kresse, G., & Hafner, J. (1994) · Physical Review B · doi:10.1103/physrevb.49.14251 · Cited by 23,019
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
High-entropy high-hardness metal carbides discovered by entropy descriptors
Sarker, P., Harrington, T., Toher, C., Oses, C., Samiee, M., Maria, J.-P., Brenner, D. W., Vecchio, K. S., & Curtarolo, S. (2018) · Nature Communications · doi:10.1038/s41467-018-07160-7 · Cited by 1,082
Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
New kagome prototype materials: discovery of KV3Sb5,RbV3Sb5 , and CsV3Sb5
Ortiz, B. R., Gomes, L. C., Morey, J. R., Winiarski, M., Bordelon, M., Mangum, J. S., Oswald, I. W. H., Rodriguez-Rivera, J. A., Neilson, J. R., Wilson, S. D., et al. (2019) · Physical Review Materials · doi:10.1103/physrevmaterials.3.094407 · Cited by 767
Metadata
- Authors
- Calderon, C. E., Plata, J. J., Toher, C., Oses, C., Levy, O., Fornari, M., Natan, A., Mehl, M. J., Hart, G., Buongiorno Nardelli, M., & Curtarolo, S.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 108
- Pages
- 233-238
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W617139115
- Cited by
- 424
- References
- 58
- FWCI
- 8.26
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Calderon, C. E., Plata, J. J., Toher, C., Oses, C., Levy, O., Fornari, M., Natan, A., Mehl, M. J., Hart, G., Buongiorno Nardelli, M., & Curtarolo, S. (2015). The AFLOW standard for high-throughput materials science calculations. Computational Materials Science. https://doi.org/10.1016/j.commatsci.2015.07.019
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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