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Rungs 1 to 4 of DFT Jacob’s ladder: Extensive test on the lattice constant, bulk modulus, and cohesive energy of solids
Open accessAbstract
A large panel of old and recently proposed exchange-correlation functionals belonging to rungs 1 to 4 of Jacob’s ladder of density functional theory are tested (with and without a dispersion correction term) for the calculation of the lattice constant, bulk modulus, and cohesive energy of solids. Particular attention will be paid to the functionals MGGA_MS2 [J. Sun et al., J. Chem. Phys. 138, 044113 (2013)], mBEEF [J. Wellendorff et al., J. Chem. Phys. 140, 144107 (2014)], and SCAN [J. Sun et al., Phys. Rev. Lett. 115, 036402 (2015)] which are meta-generalized gradient approximations (meta-GGA) and are developed with the goal to be universally good. Another goal is also to determine for which semilocal functionals and groups of solids it is beneficial (or not necessary) to use the Hartree-Fock exchange or a dispersion correction term. It is concluded that for strongly bound solids, functionals of the GGA, i.e., rung 2 of Jacob’s ladder, are as accurate as the more sophisticated functionals of the higher rungs, while it is necessary to use dispersion corrected functionals in order to expect at least meaningful results for weakly bound solids. If results for finite systems are also considered, then the meta-GGA functionals are overall clearly superior to the GGA functionals.
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Cites
Effect of the damping function in dispersion corrected density functional theory
Grimme, S., Ehrlich, S., & Goerigk, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21759 · Cited by 28,221
Density functional theory with London dispersion corrections
Grimme, S. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.30 · Cited by 2,829
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Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation
Furness, J. W., Kaplan, A. D., Ning, J., Perdew, J. P., & Sun, J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02405 · Cited by 1,438
Combining 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
CRYSTAL23: A Program for Computational Solid State Physics and Chemistry
Erba, A., Desmarais, J. K., Casassa, S., Civalleri, B., Donà, L., Bush, I. J., Searle, B., Maschio, L., Edith-Daga, L., Cossard, A., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00958 · Cited by 367
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
Self-citationDuan, C., Chen, S., Taylor, M. G., Liu, F., & Kulik, H. J. (2021) · Chemical Science · doi:10.1039/d1sc03701c · Cited by 40
Metadata
- Authors
- Tran, F., Stelzl, J., & Blaha, P.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2016-05-27
- Volume
- 144
- Issue
- 20
- Pages
- 204120-204120
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2293452066
- Cited by
- 256
- References
- 2
- FWCI
- 11.43
- Type
- journal article
- Language
- en
- Open access
- green
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- Tran, F., Stelzl, J., & Blaha, P. (2016). Rungs 1 to 4 of DFT Jacob’s ladder: Extensive test on the lattice constant, bulk modulus, and cohesive energy of solids. The Journal of Chemical Physics. https://doi.org/10.1063/1.4948636
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