The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Critical Assessment of the Performance of Density Functional Methods for Several Atomic and Molecular Properties
Open accessAbstract
The reliable prediction of molecular properties is a vital task of computational chemistry. In recent years, density functional theory (DFT) has become a popular method for calculating molecular properties for a vast array of systems varying in size from small organic molecules to large biological compounds such as proteins. In this work we assess the ability of many DFT methods to accurately determine atomic and molecular properties for small molecules containing elements commonly found in proteins, DNA, and RNA. These properties include bond lengths, bond angles, ground state vibrational frequencies, electron affinities, ionization potentials, heats of formation, hydrogen bond interaction energies, conformational energies, and reaction barrier heights. Calculations are carried out with the 3-21G*, 6-31G*, 3-21+G*, 6-31+G*, 6-31++G*, cc-pVxZ, and aug-cc-pVxZ (x=D,T) basis sets, while bond distance and bond angle calculations are also done using the cc-pVQZ and aug-cc-pVQZ basis sets. Members of the popular functional classes, namely, LSDA, GGA, meta-GGA, hybrid-GGA, and hybrid-meta-GGA, are considered in this work. For the purpose of comparison, Hartree-Fock (HF) and second order many-body perturbation (MP2) methods are also assessed in terms of their ability to determine these physical properties. Ultimately, it is observed that the split valence bases of the 6-31G variety provide accuracies similar to those of the more computationally expensive Dunning type basis sets. Another conclusion from this survey is that the hybrid-meta-GGA functionals are typically among the most accurate functionals for all of the properties examined in this work.
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Cites
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874
Cited by
Valenzano, L., Civalleri, B., Chavan, S., Bordiga, S., Nilsen, M. H., Jakobsen, S., Lillerud, K. P., & Lamberti, C. (2011) · Chemistry of Materials · doi:10.1021/cm1022882 · Cited by 1,919
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
Riley, K. E., Pitoňák, M., Jurečka, P., & Hobza, P. (2010) · Chemical Reviews · doi:10.1021/cr1000173 · Cited by 759
Self-citationMolnar, L. F., He, X., Wang, B., & Merz, K. M. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3173809 · Cited by 123
Self-citationSzczepanik, D. W., Solà, M., Andrzejak, M., Pawełek, B., Dominikowska, J., Kukułka, M., Dyduch, K., Krygowski, T. M., & Szatylowicz, H. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24805 · Cited by 107
Metadata
- Authors
- Riley, K. E., Op't Holt, B. T., & Merz, K. M.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2007-01-20
- Volume
- 3
- Issue
- 2
- Pages
- 407-433
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- OpenAlex
- W2052099987
- Cited by
- 348
- References
- 1
- FWCI
- 17.72
- Type
- journal article
- Language
- en
- Open access
- green
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