The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Density Functionals with Broad Applicability in Chemistry
Abstract
Although density functional theory is widely used in the computational chemistry community, the most popular density functional, B3LYP, has some serious shortcomings: (i) it is better for main-group chemistry than for transition metals; (ii) it systematically underestimates reaction barrier heights; (iii) it is inaccurate for interactions dominated by medium-range correlation energy, such as van der Waals attraction, aromatic-aromatic stacking, and alkane isomerization energies. We have developed a variety of databases for testing and designing new density functionals. We used these data to design new density functionals, called M06-class (and, earlier, M05-class) functionals, for which we enforced some fundamental exact constraints such as the uniform-electron-gas limit and the absence of self-correlation energy. Our M06-class functionals depend on spin-up and spin-down electron densities (i.e., spin densities), spin density gradients, spin kinetic energy densities, and, for nonlocal (also called hybrid) functionals, Hartree-Fock exchange. We have developed four new functionals that overcome the above-mentioned difficulties: (a) M06, a hybrid meta functional, is a functional with good accuracy "across-the-board" for transition metals, main group thermochemistry, medium-range correlation energy, and barrier heights; (b) M06-2X, another hybrid meta functional, is not good for transition metals but has excellent performance for main group chemistry, predicts accurate valence and Rydberg electronic excitation energies, and is an excellent functional for aromatic-aromatic stacking interactions; (c) M06-L is not as accurate as M06 for barrier heights but is the most accurate functional for transition metals and is the only local functional (no Hartree-Fock exchange) with better across-the-board average performance than B3LYP; this is very important because only local functionals are affordable for many demanding applications on very large systems; (d) M06-HF has good performance for valence, Rydberg, and charge transfer excited states with minimal sacrifice of ground-state accuracy. In this Account, we compared the performance of the M06-class functionals and one M05-class functional (M05-2X) to that of some popular functionals for diverse databases and their performance on several difficult cases. The tests include barrier heights, conformational energy, and the trend in bond dissociation energies of Grubbs' ruthenium catalysts for olefin metathesis. Based on these tests, we recommend (1) the M06-2X, BMK, and M05-2X functionals for main-group thermochemistry and kinetics, (2) M06-2X and M06 for systems where main-group thermochemistry, kinetics, and noncovalent interactions are all important, (3) M06-L and M06 for transition metal thermochemistry, (4) M06 for problems involving multireference rearrangements or reactions where both organic and transition-metal bonds are formed or broken, (5) M06-2X, M05-2X, M06-HF, M06, and M06-L for the study of noncovalent interactions, (6) M06-HF when the use of full Hartree-Fock exchange is important, for example, to avoid the error of self-interaction at long-range, (7) M06-L when a local functional is required, because a local functional has much lower cost for large systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874
Cited by
Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3382344 · Cited by 59,686
Density functional theory with London dispersion corrections
Grimme, S. (2011) · WIREs Computational Molecular Science · doi:10.1002/wcms.30 · Cited by 2,829
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
Density functional theory for transition metals and transition metal chemistry
Cramer, C. J., & Truhlar, D. G. (2009) · Physical Chemistry Chemical Physics · doi:10.1039/b907148b · Cited by 1,648
Self-citationLaurent, A. D., & Jacquemin, D. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24438 · Cited by 1,382
Defining the hydrogen bond: An account (IUPAC Technical Report)
Arunan, E., Desiraju, G. R., Klein, R. A., Sadlej, J., Scheiner, S., Alkorta, I., Clary, D. C., Crabtree, R. H., Dannenberg, J. J., Hobza, P., et al. (2011) · Pure and Applied Chemistry · doi:10.1351/pac-rep-10-01-01 · Cited by 1,021
S66: A Well-balanced Database of Benchmark Interaction Energies Relevant to Biomolecular Structures
Řezáč, J., Riley, K. E., & Hobza, P. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct2002946 · Cited by 1,019
B97-3c: A revised low-cost variant of the B97-D density functional method
Brandenburg, J. G., Bannwarth, C., Hansen, A., & Grimme, S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5012601 · Cited by 760
Riley, K. E., Pitoňák, M., Jurečka, P., & Hobza, P. (2010) · Chemical Reviews · doi:10.1021/cr1000173 · Cited by 759
Peverati, R., & Truhlar, D. G. (2014) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2012.0476 · Cited by 734
Self-citationMiar, M., Shiroudi, A., Pourshamsian, K., Oliaey, A. R., & Hatamjafari, F. (2021) · Journal of Chemical Research · doi:10.1177/1747519820932091 · Cited by 580
Lejaeghere, K., Van Speybroeck, V., Van Oost, G., & Cottenier, S. (2014) · Critical Reviews in Solid State and Materials Sciences · doi:10.1080/10408436.2013.772503 · Cited by 560
Improta, R., Santoro, F., & Blancafort, L. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00444 · Cited by 499
Theoretical Study of the Remarkably Diverse Linkages in Lignin
Parthasarathi, R., Romero, R. A., Redondo, A., & Gnanakaran, S. (2011) · The Journal of Physical Chemistry Letters · doi:10.1021/jz201201q · Cited by 433
Ren, H., Zhang, Z., Cheng, X., Zou, Z., Chen, X., & He, C. (2023) · Science Advances · doi:10.1126/sciadv.adh4327 · Cited by 431
Halogenated Zn2+ Solvation Structure for Reversible Zn Metal Batteries
Zhang, Q., Ma, Y., Lu, Y., Ni, Y., Lin, L., Hao, Z., Yan, Z., Zhao, Q., & Chen, J. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c06927 · Cited by 426
Willoughby, P. H., Jansma, M. J., & Hoye, T. R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.042 · Cited by 404
Kozuch, S., Gruzman, D., & Martin, J. M. L. (2010) · The Journal of Physical Chemistry C · doi:10.1021/jp1070852 · Cited by 398
KO t Bu: A Privileged Reagent for Electron Transfer Reactions?
Barham, J. P., Coulthard, G., Emery, K. J., Doni, E., Cumine, F., Nocera, G., John, M. P., Berlouis, L. E. A., McGuire, T., Tuttle, T., et al. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b03282 · Cited by 339
Computational prediction of small-molecule catalysts
Houk, K. N., & Cheong, P. H.-Y. (2008) · Nature · doi:10.1038/nature07368 · Cited by 329
Understanding chemical reactivity using the activation strain model
Vermeeren, P., van der Lubbe, S. C. C., Fonseca Guerra, C., Bickelhaupt, F. M., & Hamlin, T. A. (2020) · Nature Protocols · doi:10.1038/s41596-019-0265-0 · Cited by 322
Beste, A., & Buchanan, A. C. (2009) · The Journal of Organic Chemistry · doi:10.1021/jo9001307 · Cited by 168
Hill, P. S., Tripati, A. K., & Schauble, E. A. (2014) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2013.06.018 · Cited by 155
Dyer, H. E., Huijser, S., Susperregui, N., Bonnet, F., Schwarz, A. D., Duchateau, R., Maron, L., & Mountford, P. (2010) · Organometallics · doi:10.1021/om100513j · Cited by 158
Shi, J., Ehteshami, N., Ma, J., Zhang, H., Liu, H., Zhang, X., Li, J., & Paillard, E. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.04.113 · Cited by 147
Sanchez-Sanchez, A., Rivilla, I., Agirre, M., Basterretxea, A., Etxeberria, A., Veloso, A., Sardon, H., Mecerreyes, D., & Cossío, F. P. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b13080 · Cited by 93
Ding, K., Miranda, M. O., Moscato-Goodpaster, B., Ajellal, N., Breyfogle, L. E., Hermes, E. D., Schaller, C. P., Roe, S. E., Cramer, C. J., Hillmyer, M. A., et al. (2012) · Macromolecules · doi:10.1021/ma301130b · Cited by 87
Gridnev, I. D., & Vorobiev, A. K. (2012) · ACS Catalysis · doi:10.1021/cs300497h · Cited by 84
Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage
Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00774 · Cited by 68
Tabthong, S., Nanok, T., Sumrit, P., Kongsaeree, P., Prabpai, S., Chuawong, P., & Hormnirun, P. (2015) · Macromolecules · doi:10.1021/acs.macromol.5b01381 · Cited by 64
Fang, J., Yu, I., Mehrkhodavandi, P., & Maron, L. (2013) · Organometallics · doi:10.1021/om400399k · Cited by 59
Guanine holes are prominent targets for mutation in cancer and inherited disease
Bacolla, A., Temiz, N. A., Yi, M., Ivanic, J., Cer, R. Z., Donohue, D. E., Ball, E. V., Mudunuri, U. S., Wang, G., Jain, A., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003816 · Cited by 40
Ortuño, M. A., Dereli, B., Chiaie, K. R. D., Biernesser, A. B., Qi, M., Byers, J. A., & Cramer, C. J. (2018) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.7b02964 · Cited by 35
Hill, P. S., Schauble, E. A., & Tripati, A. (2020) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2019.10.016 · Cited by 29
Chandanabodhi, D., & Nanok, T. (2017) · Molecular Catalysis · doi:10.1016/j.mcat.2017.04.005 · Cited by 31
Nakonkhet, C., Nanok, T., Wattanathana, W., Chuawong, P., & Hormnirun, P. (2017) · Dalton Transactions · doi:10.1039/c7dt02435e · Cited by 31
Rosal, I. D., Poteau, R., & Maron, L. (2011) · Dalton Transactions · doi:10.1039/c1dt10566c · Cited by 27
Rosal, I. D., Poteau, R., & Maron, L. (2011) · Dalton Transactions · doi:10.1039/c1dt10567a · Cited by 25
A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.
Yang, Z., Liu, Y., Chen, Z., Xu, Z., Shi, J., Chen, K., & Zhu, W. (2015) · Journal of Molecular Modeling · doi:10.1007/s00894-015-2681-6 · Cited by 23
Gribanova, T. N., Minyaev, R. M., & Minkin, V. I. (2016) · Structural Chemistry · doi:10.1007/s11224-016-0748-3 · Cited by 5
Metadata
- Authors
- Zhao, Y., & Truhlar, D. G.
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-01-11
- Volume
- 41
- Issue
- 2
- Pages
- 157-167
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W2085336624
- Cited by
- 7,133
- References
- 3
- FWCI
- 171.13
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.