Pseudopotentials for high-throughput DFT calculations AITranslate
Abstract AITranslate
Highlights • We present design criteria for high-throughput pseudopotentials. • We present and test the GBRV pseudopotential library. • We draw conclusions about the accuracy of modern pseudopotentials. The increasing use of high-throughput density-functional theory (DFT) calculations in the computational design and optimization of materials requires the availability of a comprehensive set of soft and transferable pseudopotentials. Here we present design criteria and testing results for a new open-source “GBRV” ultrasoft pseudopotential library that has been optimized for use in high-throughput DFT calculations. We benchmark the GBRV potentials, as well as two other pseudopotential sets available in the literature, to all-electron calculations in order to validate their accuracy. The results allow us to draw conclusions about the accuracy of modern pseudopotentials in a variety of chemical environments.
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DOI:https://doi.org/10.1016/j.commatsci.2013.08.053
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Highlights • We present design criteria for high-throughput pseudopotentials. • We present and test the GBRV pseudopotential library. • We draw conclusions about the accuracy of modern pseudopotentials. The increasing use of high-throughput density-functional theory (DFT) calculations in the computational design and optimization of materials requires the availability of a comprehensive set of soft and transferable pseudopotentials. Here we present design criteria and testing results for a new open-source “GBRV” ultrasoft pseudopotential library that has been optimized for use in high-throughput DFT calculations. We benchmark the GBRV potentials, as well as two other pseudopotential sets available in the literature, to all-electron calculations in order to validate their accuracy. The results allow us to draw conclusions about the accuracy of modern pseudopotentials in a variety of chemical environments.
quote
| GB/T 7714-2015 | [1] Kevin F. Garrity, Joseph W. Bennett, Karin M. Rabe, et al. Computational Materials Science, 2014(81). DOI:10.1016/j.commatsci.2013.08.053. |
| MLA | [1] Kevin F. Garrity, et al., Computational Materials Science, no. 81, 2014, https://doi.org/10.1016/j.commatsci.2013.08.053. |
| APA | [1] Kevin F. Garrity, Joseph W. Bennett, Karin M. Rabe, & David Vanderbilt. (2014). Computational Materials Science(81). https://doi.org/10.1016/j.commatsci.2013.08.053 |
| IEEE | [1] Kevin F. Garrity, Joseph W. Bennett, Karin M. Rabe, and David Vanderbilt, Computational Materials Science, no. 81, 2014, doi: 10.1016/j.commatsci.2013.08.053. |
