Revised M06 density functional for main-group and transition-metal chemistry

We present a hybrid metageneralized-gradient-approximation functional, revM06, which is based on adding Hartree–Fock exchange to the revM06-L functional form. Compared with the original M06 suite of density functionals, the resulting revM06 functional has significantly improved across-the-board accu...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 115; no. 41; pp. 10257 - 10262
Main Authors Wang, Ying, Verma, Pragya, Jin, Xinsheng, Truhlar, Donald G., He, Xiao
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 09.10.2018
National Academy of Sciences, Washington, DC (United States)
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Summary:We present a hybrid metageneralized-gradient-approximation functional, revM06, which is based on adding Hartree–Fock exchange to the revM06-L functional form. Compared with the original M06 suite of density functionals, the resulting revM06 functional has significantly improved across-the-board accuracy for both main-group and transition-metal chemistry. The revM06 functional improves on the M06-2X functional for main-group and transition-metal bond energies, atomic excitation energies, isomerization energies of large molecules, molecular structures, and both weakly and strongly correlated atomic and molecular data, and it shows a clear improvement over M06 and M06-2X for non-covalent interactions, including smoother potential curves for rare-gas dimers. The revM06 functional also predicts more accurate results than M06 and M06-2X for most of the outside-the-training-set test sets examined in this study. Therefore, the revM06 functional is well-suited for a broad range of chemical applications for both main-group and transition-metal elements.
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SC0015997; SC0008688; FG02-17ER16362
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences, and Biosciences Division
USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
Contributed by Donald G. Truhlar, August 11, 2018 (sent for review June 27, 2018; reviewed by K. N. Houk and Markus Reiher)
Author contributions: D.G.T. and X.H. designed research; Y.W., P.V., X.J., D.G.T., and X.H. performed research; Y.W., P.V., X.J., D.G.T., and X.H. analyzed data; and Y.W., P.V., X.J., D.G.T., and X.H. wrote the paper.
Reviewers: K.N.H., University of California, Los Angeles; and M.R., Swiss Federal Institute of Technology.
1Y.W. and P.V. contributed equally to this work.
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.1810421115