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Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
Open accessAbstract
In the past 30 years, Kohn–Sham density functional theory has emerged as the most popular electronic structuremethod in computational chemistry. To assess the ever-increasing number of approximate exchange-correlation functionals, this review benchmarks a total of 200 density functionals on a molecular database (MGCDB84) of nearly 5000 data points. The database employed, provided as Supplemental Data, is comprised of 84 data-sets and contains non-covalent interactions, isomerisation energies, thermochemistry, and barrier heights. In addition, the evolution of nonempirical and semi-empirical density functional design is reviewed, and guidelines are provided for the proper and effective use of density functionals. The most promising functional considered is ωB97M-V, a range-separated hybrid meta-GGA with VV10 nonlocal correlation, designed using a combinatorial approach. From the local GGAs, B97-D3, revPBE-D3, and BLYP-D3 are recommended, while from the local meta-GGAs, B97M-rV is the leading choice, followed by MS1-D3 and M06-LD3. The best hybrid GGAs are ωB97X-V, ωB97X-D3, and ωB97X-D, while useful hybrid meta-GGAs (besides ωB97M-V) include ωM05-D, M06-2X-D3, and MN15. Ultimately, today’s state-of-the-art functionals are close to achieving the level of accuracy desired for a broad range of chemical applications, and the principal remaining limitations are associated with systems that exhibit significant selfinteraction/ delocalisation errors and/or strong correlation effects.
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“Such small errors for activation barriers can be considered to lie within DFT accuracy which is on average about 20–30 kJ mol−1 with respect to coupled cluster calculations in the case of (meta)GGA functionals74.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [74]
In the citing paper
“For the higher-level reference, we chose the range-separated hybrid DFT functional ωB97X complemented with the empirical dispersion correction D3(BJ): a functional that shows considerably better performance for water cluster binding energies and reaction barriers74,77,78 than our baseline reference SCAN+D3(BJ) functional.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [74]
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
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
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 exchange-energy approximation with correct asymptotic behavior
Becke, A. D. (1988) · Physical Review A · doi:10.1103/physreva.38.3098 · Cited by 54,100
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction
Grimme, S. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20495 · Cited by 31,214
Zhao, Y., & Truhlar, D. G. (2007) · Theoretical Chemistry Accounts · doi:10.1007/s00214-007-0310-x · Cited by 30,666
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
Research works printed but not linked: 1
- Peng H., 2016, Phys. Rev. X, 6, 041005
Cited by
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Goerigk, L., Hansen, A., Bauer, C. A., Ehrlich, S., Najibi, A., & Grimme, S. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp04913g · Cited by 1,929
Software for the frontiers of quantum chemistry: An overview of developments in the Q-Chem 5 package
Epifanovsky, E., Gilbert, A. T. B., Feng, X., Lee, J., Mao, Y., Mardirossian, N., Pokhilko, P., White, A. F., Coons, M. P., Dempwolff, A. L., et al. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0055522 · Cited by 1,387
Self-citationBest‐Practice DFT Protocols for Basic Molecular Computational Chemistry**
Bursch, M., Mewes, J., Hansen, A., & Grimme, S. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202205735 · Cited by 958
Recent developments in libxc — A comprehensive library of functionals for density functional theory
Lehtola, S., Steigemann, C., Oliveira, M. J. T., & Marques, M. A. L. (2017) · SoftwareX · doi:10.1016/j.softx.2017.11.002 · Cited by 916
Liu, Z., Lu, T., & Chen, Q. (2020) · Carbon · doi:10.1016/j.carbon.2020.09.048 · Cited by 827
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 776
Smith, J. S., Nebgen, B. T., Zubatyuk, R. I., Lubbers, N., Devereux, C., Barros, K. M., Tretiak, S., Isayev, O., & Roitberg, A. E. (2019) · Nature Communications · doi:10.1038/s41467-019-10827-4 · Cited by 702
Santra, G., Sylvetsky, N., & Martin, J. M. L. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b03157 · Cited by 527
Computational Methods in Heterogeneous Catalysis
Chen, B. W. J., Xu, L., & Mavrikakis, M. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01060 · Cited by 465
Nelson, T., White, A. J., Bjorgaard, J. A., Sifain, A. E., Zhang, Y., Nebgen, B. T., Fernández-Alberti, S., Mozyrsky, D., Roitberg, A. E., & Tretiak, S. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00447 · Cited by 453
Density Functional Theory for Electrocatalysis
Liao, X., Lu, R., Xia, L., Liu, Q., Wang, H., Zhao, K., Wang, Z., & Zhao, Y. (2021) · Energy & environment materials · doi:10.1002/eem2.12204 · Cited by 444
Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
Vogiatzis, K. D., Polynski, M. V., Kirkland, J. K., Townsend, J., Hashemi, A., Liu, C., & Pidko, E. A. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00361 · Cited by 440
Molecular Dynamics Simulations of Membrane Permeability
Venable, R. M., Krämer, A., & Pastor, R. W. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00486 · Cited by 401
Chemical Bonding and Bonding Models of Main-Group Compounds
Zhao, L., Pan, S., Holzmann, N., Schwerdtfeger, P., & Frenking, G. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00722 · Cited by 376
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. G., Maschio, L., Daga, L. E., Cossard, A., et al. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00958 · Cited by 376
Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**
Bursch, M., Mewes, J., Hansen, A., & Grimme, S. (2022) · Angewandte Chemie · doi:10.1002/ange.202205735 · Cited by 345
Najibi, A., & Goerigk, L. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00842 · Cited by 320
Allendorf, M. D., Hulvey, Z., Gennett, T., Ahmed, A., Autrey, T. S., Camp, J. S., Cho, E. S., Furukawa, H., Harańczyk, M., Head‐Gordon, M., et al. (2018) · Energy & Environmental Science · doi:10.1039/c8ee01085d · Cited by 317
Self-citationEven More Efficient Quantum Computations of Chemistry Through Tensor Hypercontraction
Lee, J., Berry, D. W., Gidney, C., Huggins, W. J., McClean, J. R., Wiebe, N., & Babbush, R. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.030305 · Cited by 304
Liakos, D. G., Guo, Y., & Neese, F. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b05734 · Cited by 304
DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science
Teale, A. M., Helgaker, T. U., Andreas Savin, B., Adamo, C., Aradi, B., Arbuznikov, A. V., Ayers, P. W., Baerends, E. J., Barone, V., Calaminici, P., et al. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp02827a · Cited by 302
Hait, D., & Head‐Gordon, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b01252 · Cited by 300
Self-citationComment on “18 and 12 – Member carbon rings (cyclo[n]carbons) – A density functional study”
Lu, T., Liu, Z., & Chen, Q. (2021) · Materials Science and Engineering B · doi:10.1016/j.mseb.2021.115425 · Cited by 283
Martin, R. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108555586 · Cited by 291
Quantum computing enhanced computational catalysis
von Burg, V., Low, G. H., Häner, T., Steiger, D. S., Reiher, M., Roetteler, M., & Troyer, M. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033055 · Cited by 310
Quantum computing enhanced computational catalysis
von Burg, V., Low, G. H., Häner, T., Steiger, D. S., Reiher, M., Roetteler, M., & Troyer, M. (2020) · arXiv · doi:10.48550/arxiv.2007.14460 · Cited by 258
Combining Wave Function Methods with Density Functional Theory for Excited States
Ghosh, S., Verma, P., Cramer, C. J., Gagliardi, L., & Truhlar, D. G. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00193 · Cited by 258
Classical dynamical density functional theory: from fundamentals to applications
Vrugt, M. T., Löwen, H., & Wittkowski, R. (2020) · Advances In Physics · doi:10.1080/00018732.2020.1854965 · Cited by 257
Computational chemistry for water-splitting electrocatalysis
Miao, L., Jia, W., Cao, X., & Jiao, L. (2024) · Chemical Society Reviews · doi:10.1039/d2cs01068b · Cited by 241
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Anstine, D. M., & Isayev, O. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c06778 · Cited by 257
Completing density functional theory by machine learning hidden messages from molecules
Nagai, R., Akashi, R., & Sugino, O. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0310-0 · Cited by 245
Computational optimization of electric fields for better catalysis design
Welborn, V. V., Pestana, L. R., & Head-Gordon, T. L. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0109-2 · Cited by 230
Machine learning accurate exchange and correlation functionals of the electronic density
Dick, S., & Fernández-Serra, M. (2020) · Nature Communications · doi:10.1038/s41467-020-17265-7 · Cited by 217
Jaramillo, D. E., Jiang, H. Z. H., Evans, H. A., Chakraborty, R., Furukawa, H., Brown, C. M., Head‐Gordon, M., & Long, J. R. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c01883 · Cited by 213
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Vargas–Hernández, R. A. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c01375 · Cited by 214
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Hait, D., & Head‐Gordon, M. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00744 · Cited by 208
Self-citationMardirossian, N., & Head‐Gordon, M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5025226 · Cited by 206
Self-citationDelocalization error: The greatest outstanding challenge in density‐functional theory
Bryenton, K. R., Adeleke, A. A., Dale, S. G., & Johnson, E. R. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1631 · Cited by 208
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Eastman, P. K., Behara, P. K., Dotson, D., Galvelis, R., Herr, J. E., Horton, J. T., Mao, Y., Chodera, J. D., Pritchard, B. P., Wang, Y., et al. (2023) · Scientific Data · doi:10.1038/s41597-022-01882-6 · Cited by 241
Najibi, A., & Goerigk, L. (2020) · Journal of Computational Chemistry · doi:10.1002/jcc.26411 · Cited by 200
Tubman, N. M., Freeman, C. D., Levine, D. S., Hait, D., Head‐Gordon, M., & Whaley, K. B. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00536 · Cited by 192
Self-citationHait, D., & Head‐Gordon, M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01127 · Cited by 186
Self-citationRoadmap on Machine learning in electronic structure
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Liang, J., Feng, X., Hait, D., & Head‐Gordon, M. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00160 · Cited by 176
Self-citationReplacing hybrid density functional theory: motivation and recent advances
Janesko, B. G. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01074j · Cited by 178
Müller, M., Hansen, A., & Grimme, S. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0133026 · Cited by 174
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Bursch, M., Caldeweyher, E., Hansen, A., Neugebauer, H., Ehlert, S., & Grimme, S. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00505 · Cited by 170
Computational thermodynamics and its applications
Liu, Z. (2020) · Acta Materialia · doi:10.1016/j.actamat.2020.08.008 · Cited by 172
Kohn-Sham Equations as Regularizer: Building Prior Knowledge into Machine-Learned Physics
Li, L., Hoyer, S., Pederson, R. D., Sun, R., Cubuk, E. D., Riley, P., & Burke, K. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.036401 · Cited by 181
Deep Learning of Activation Energies
Grambow, C. A., Pattanaik, L., & Green, W. H. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c00500 · Cited by 175
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Véril, M., Scemama, A., Caffarel, M., Lipparini, F., Boggio‐Pasqua, M., Jacquemin, D., & Loos, P. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1517 · Cited by 166
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Fedik, N. S., Zubatyuk, R. I., Kulichenko, M., Lubbers, N., Smith, J. S., Nebgen, B. T., Messerly, R. A., Li, Y. W., Boldyrev, A. I., Barros, K. M., et al. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00416-3 · Cited by 173
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Shao, Y., Mei, Y., Sundholm, D., & Kaila, V. R. I. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00823 · Cited by 165
Huaulmé, Q., Mwalukuku, V. M., Joly, D., Liotier, J., Kervella, Y., Maldivi, P., Narbey, S., Oswald, F., Riquelme, A. J., Anta, J. A., et al. (2020) · Nature Energy · doi:10.1038/s41560-020-0624-7 · Cited by 165
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Herbert, J. M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5126216 · Cited by 163
Phadke, N. M., Mansoor, E., Bondil, M., Head‐Gordon, M., & Bell, A. T. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b11443 · Cited by 161
Self-citationRathi, P. C., Ludlow, R. F., & Verdonk, M. L. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01129 · Cited by 159
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Tesch, R., & Kowalski, P. M. (2022) · Physical Review B · doi:10.1103/physrevb.105.195153 · Cited by 158
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Prentice, J. C. A., Aarons, J., Womack, J. C., Allen, A. E. A., Ανδρινόπουλος, Λ., Anton, L., Bell, R. A., Bhandari, A., Bramley, G. A., Charlton, R. J., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0004445 · Cited by 157
Grambow, C. A., Pattanaik, L., & Green, W. H. (2020) · Scientific Data · doi:10.1038/s41597-020-0460-4 · Cited by 160
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Goerigk, L., & Mehta, N. (2019) · Australian Journal of Chemistry · doi:10.1071/ch19023 · Cited by 156
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Mehta, N., Casanova‐Páez, M., & Goerigk, L. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp03852j · Cited by 152
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Li, G., & Pidko, E. A. (2018) · ChemCatChem · doi:10.1002/cctc.201801493 · Cited by 154
Chan, B., Gill, P. M. W., & Kimura, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00239 · Cited by 149
Cruz‐Martínez, H., Rojas-Chávez, H., Matadamas-Ortíz, P. T., Ortiz-Herrera, J. C., López-Chávez, E., Solorza‐Feria, O., & Medina, D. I. (2021) · Materials Today Physics · doi:10.1016/j.mtphys.2021.100406 · Cited by 149
Mester, D., & Kállay, M. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01307 · Cited by 150
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Fu, L., Zhang, Z., Chen, P., Lin, Z., & Liu, G. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c05373 · Cited by 146
Hait, D., & Head‐Gordon, M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp03569e · Cited by 148
Self-citationLevine, D. S., Hait, D., Tubman, N. M., Lehtola, S., Whaley, K. B., & Head‐Gordon, M. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b01255 · Cited by 142
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Wen, M., Spotte‐Smith, E. W. C., Blau, S. M., McDermott, M. J., Krishnapriyan, A. S., & Persson, K. A. (2023) · Nature Computational Science · doi:10.1038/s43588-022-00369-z · Cited by 147
Electron density learning of non-covalent systems
Fabrizio, A., Grisafi, A., Meyer, B., Ceriotti, M., & Corminbœuf, C. (2019) · Chemical Science · doi:10.1039/c9sc02696g · Cited by 138
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Maurer, R. J., Freysoldt, C., Reilly, A. M., Brandenburg, J. G., Hofmann, O. T., Björkman, T., Lebègue∥, S., & Tkatchenko, A. (2019) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010143 · Cited by 140
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Mao, Y., Loipersberger, M., Horn, P. R., Das, A. K., Demerdash, O., Levine, D. S., Veccham, S. P., Head-Gordon, T. L., & Head‐Gordon, M. (2021) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-090419-115149 · Cited by 134
Self-citationHaldhar, R., Prasad, D., Bahadur, I., Dagdag, O., & Бериша, А. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.114958 · Cited by 129
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Al-Hamdani, Y. S., & Tkatchenko, A. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5075487 · Cited by 127
Ning, J., Kothakonda, M., Furness, J. W., Kaplan, A. D., Ehlert, S., Brandenburg, J. G., Perdew, J. P., & Sun, J. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.106.075422 · Cited by 127
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Akbashev, A. R. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c00123 · Cited by 125
Lee, J., & Head‐Gordon, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00731 · Cited by 124
Self-citationMorgante, P., & Peverati, R. (2020) · International Journal of Quantum Chemistry · doi:10.1002/qua.26332 · Cited by 123
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Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Zhao, X., Yan, K., et al. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000115 · Cited by 214
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Sim, E., Song, S., Vuckovic, S., & Burke, K. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c11506 · Cited by 126
Hait, D., & Head‐Gordon, M. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b02417 · Cited by 122
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Mansoor, E., Head‐Gordon, M., & Bell, A. T. (2018) · ACS Catalysis · doi:10.1021/acscatal.7b04295 · Cited by 115
Self-citationHiemstra, T. (2018) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2018.07.017 · Cited by 112
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Williams, K. T., Yao, Y., Li, J., Chen, L., Shi, H., Motta, M., Niu, C., Ray, U., Guo, S., Anderson, R., et al. (2020) · Physical Review X · doi:10.1103/physrevx.10.011041 · Cited by 123
Dagdag, O., Бериша, А., Mehmeti, V., Haldhar, R., Berdimurodov, E. T., Hamed, O. A., Jodeh, S. W., Lgaz, H., Sherif, E. M., & Ebenso, E. E. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.117886 · Cited by 110
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Metadata
- Authors
- Mardirossian, N., & Head-Gordon, M.
- Affiliations
- University of California, Berkeley (US)
- Kenneth S. Pitzer Center for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, CA, USA
- ORCID
- Mardirossian, Narbe
- Venue
- Molecular Physics
- Publisher
- Informa UK Limited
- Published
- 2017-06-21
- Volume
- 115
- Issue
- 19
- Pages
- 2315-2372
- ISSN
- 0026-8976 (ISSN-L)
- 1362-3028
- OpenAlex
- W2639728117
- Cited by
- 2,307
- References
- 258
- Citations by year
- 2026: 269 · 2025: 288 · 2024: 283 · 2023: 288 · 2022: 280 · 2021: 285 · 2020: 297 · 2019: 195 · 2018: 111 · 2017: 8
- FWCI
- 81.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular Physics (publishedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Basic Energy Sciences (DE-AC02- 05CH11231)
- MURI (W911NF-14-1-0359)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Chemistry › Organic Chemistry › Physical Organic Chemistry and Reaction Kinetics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- density functionals, exchange-correlation functionals, functional benchmarking, non-covalent interactions, thermochemistry, reaction barrier height, range-separated hybrid functionals, meta-GGA, self-interaction error, strong correlation effects, Kohn-Sham theory, electronic structure methods
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