The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Optimization algorithm for the generation of ONCV pseudopotentials
Open accessAbstract
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder–Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials (http://www.quantum-simulation.org) provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry.
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“All the tested PSP libraries are based on the generalized gradient approximation (GGA) for the exchangecorrelation functional of Perdew, Burke and Ernzerhof (PBE) [8] and they include the three main pseudization approaches [12]: norm-conserving (NC) [13], ultrasoft (US) [14] and projector-augmented wave (PAW) [15].”
From Precision and efficiency in solid-state pseudopotential calculations (2018) · cites this paper as [13]
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Cites
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The PseudoDojo: Training and grading a 85 element optimized norm-conserving pseudopotential table
van Setten, M. J., Giantomassi, M., Bousquet, É., Verstraete, M. J., Hamann, D. R., Gonze, X., & Rignanese, G. (2018) · Computer Physics Communications · doi:10.1016/j.cpc.2018.01.012 · Cited by 2,263
Q uantum ESPRESSO toward the exascale
Giannozzi, P., Baseggio, O., Bonfà, P., Brunato, D., Car, R., Carnimeo, I., Cavazzoni, C., de Gironcoli, S., Delugas, P., Ruffino, F. F., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0005082 · Cited by 2,197
QuantumATK: an integrated platform of electronic and atomic-scale modelling tools
Smidstrup, S., Markussen, T., Vancraeyveld, P., Wellendorff, J., Schneider, J., Gunst, T., Verstichel, B., Stradi, D., Khomyakov, P. A., Vej-Hansen, U. G., et al. (2019) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ab4007 · Cited by 2,015
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
Reproducibility in density functional theory calculations of solids
Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science · doi:10.1126/science.aad3000 · Cited by 1,649
Self-citation
Metadata
- Authors
- Schlipf, M., & Gygi, F.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Schlipf, Martin
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 196
- Pages
- 36-44
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W1695703141
- Cited by
- 1,300
- References
- 27
- Citations by year
- 2026: 145 · 2025: 193 · 2024: 158 · 2023: 147 · 2022: 155 · 2021: 141 · 2020: 120 · 2019: 109 · 2018: 80 · 2017: 39 · 2016: 11 · 2015: 2
- FWCI
- 22.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer Physics Communications (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Science (DE-AC02-06CH11357)
- Argonne National Laboratory
- U.S. Department of Energy (DOE-BES DE-SC0008938)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Materials Science › Metals and Alloys › Thermodynamic and Structural Properties of Metals and Alloys
- Keywords
- norm-conserving pseudopotentials, Nelder-Mead algorithm, quality function, FLAPW, FLEUR, periodic table
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