The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Parametrization and Benchmark of DFTB3 for Organic Molecules
Abstract
DFTB3 is a recent extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB) and derived from a third order expansion of the density functional theory (DFT) total energy around a given reference density. Being applied in combination with the parametrization of its predecessor (MIO), DFTB3 improves for hydrogen binding energies, proton affinities, and hydrogen transfer barriers. In the present study, parameters especially designed for DFTB3 are presented, and its performance is evaluated for small organic molecules focusing on thermochemistry, geometries, and vibrational frequencies from our own and several databases from literature. The new parameters remove significant overbinding errors, reduce errors for geometries of noncovalent interactions, and improve the overall performance.
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Cites
Generalized Gradient Approximation Made Simple
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Cited by
Christensen, A. S., Kubař, T., Cui, Q., & Elstner, M. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00584 · Cited by 446
Self-citationBarely porous organic cages for hydrogen isotope separation
Liu, M., Zhang, L., Little, M. A., Kapil, V., Ceriotti, M., Yang, S., Ding, L., Holden, D. L., Balderas-Xicohténcatl, R., He, D., et al. (2019) · Science · doi:10.1126/science.aax7427 · Cited by 399
Defect Engineering and Surface Functionalization of Nanocarbons for Metal‐Free Catalysis
Ortiz‐Medina, J., Wang, Z., Cruz‐Silva, R., Morelos‐Gomez, A., Wang, F., Yao, X., Terrones, M., & Endo, M. (2019) · Advanced Materials · doi:10.1002/adma.201805717 · Cited by 245
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63
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Metadata
- Authors
- Gaus, M., Goez, A., & Elstner, M.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-11-26
- Volume
- 9
- Issue
- 1
- Pages
- 338-354
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2327161550
- Cited by
- 1,015
- References
- 3
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- closed
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