Selecting the appropriate density functional for one’s molecular system at hand is often a very confounding problem, especially for non-expert or first-time users of computational chemistry. The DFT zoo is vast and confusing, and perhaps what makes the situation worse is that there is no lack of benchmarking studies. For example, I have made more than 30 posts on benchmark studies, and I made no attempt to be comprehensive over the past dozen years!
One such benchmark study that I missed was presented by Mardirossian and Head-Gordon in 2017.1 They evaluated 200 density functional using the MGCDB84 database, a combination of data from a number of different groups. They make a series of recommendations for local GGA, local meta-GGA, hybrid GGA, and hybrid meta-GGA functionals. And when pressed to choose just one functional overall, they opt for ωB97M-V, a range-separated hybrid meta-GGA with VV10 nonlocal correlation.
Georigk and Mehta2 just recently offer a review of the density functional zoo. Leaning heavily on benchmark studies using the GMTKN553 database, they report a number of observations. Of no surprise to readers of this blog, their main conclusion is that accounting for London dispersion is essential, usually through some type of correction like those proposed by Grimme.
These authors also note the general disparity between the most accurate, best performing functional per the benchmark studies and the results of the DFT poll conducted for many years by Swart, Bickelhaupt and Duran. It is somewhat remarkable that PBE or PBE0 have topped the poll for many years, despite the fact that many newer functionals perform better. As always, when choosing a functional caveat emptor.
References
1. Mardirossian, N.; Head-Gordon, M., “Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals.” Mol. Phys. 2017, 115, 2315-2372, DOI: 10.1080/00268976.2017.1333644.
2. Goerigk, L.; Mehta, N., “A Trip to the Density Functional Theory Zoo: Warnings and Recommendations for the User.” Aust. J. Chem. 2019, ASAP, DOI: 10.1071/CH19023.
3. Goerigk, L.; Hansen, A.; Bauer, C.; Ehrlich, S.; Najibi, A.; Grimme, S., “A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions.” Phys. Chem. Chem. Phys. 2017, 19, 32184-32215, DOI: 10.1039/C7CP04913G.
from Computational Organic Chemistry https://ift.tt/2HpXW2F
Selecting the appropriate density functional for one’s molecular system at hand is often a very confounding problem, especially for non-expert or first-time users of computational chemistry. The DFT zoo is vast and confusing, and perhaps what makes the situation worse is that there is no lack of benchmarking studies. For example, I have made more than 30 posts on benchmark studies, and I made no attempt to be comprehensive over the past dozen years!
One such benchmark study that I missed was presented by Mardirossian and Head-Gordon in 2017.1 They evaluated 200 density functional using the MGCDB84 database, a combination of data from a number of different groups. They make a series of recommendations for local GGA, local meta-GGA, hybrid GGA, and hybrid meta-GGA functionals. And when pressed to choose just one functional overall, they opt for ωB97M-V, a range-separated hybrid meta-GGA with VV10 nonlocal correlation.
Georigk and Mehta2 just recently offer a review of the density functional zoo. Leaning heavily on benchmark studies using the GMTKN553 database, they report a number of observations. Of no surprise to readers of this blog, their main conclusion is that accounting for London dispersion is essential, usually through some type of correction like those proposed by Grimme.
These authors also note the general disparity between the most accurate, best performing functional per the benchmark studies and the results of the DFT poll conducted for many years by Swart, Bickelhaupt and Duran. It is somewhat remarkable that PBE or PBE0 have topped the poll for many years, despite the fact that many newer functionals perform better. As always, when choosing a functional caveat emptor.
References
1. Mardirossian, N.; Head-Gordon, M., “Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals.” Mol. Phys. 2017, 115, 2315-2372, DOI: 10.1080/00268976.2017.1333644.
2. Goerigk, L.; Mehta, N., “A Trip to the Density Functional Theory Zoo: Warnings and Recommendations for the User.” Aust. J. Chem. 2019, ASAP, DOI: 10.1071/CH19023.
3. Goerigk, L.; Hansen, A.; Bauer, C.; Ehrlich, S.; Najibi, A.; Grimme, S., “A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions.” Phys. Chem. Chem. Phys. 2017, 19, 32184-32215, DOI: 10.1039/C7CP04913G.
from Computational Organic Chemistry https://ift.tt/2HpXW2F
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