Literature DB >> 29110012

A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions.

Lars Goerigk1, Andreas Hansen, Christoph Bauer, Stephan Ehrlich, Asim Najibi, Stefan Grimme.   

Abstract

We present the GMTKN55 benchmark database for general main group thermochemistry, kinetics and noncovalent interactions. Compared to its popular predecessor GMTKN30 [Goerigk and Grimme J. Chem. Theory Comput., 2011, 7, 291], it allows assessment across a larger variety of chemical problems-with 13 new benchmark sets being presented for the first time-and it also provides reference values of significantly higher quality for most sets. GMTKN55 comprises 1505 relative energies based on 2462 single-point calculations and it is accessible to the user community via a dedicated website. Herein, we demonstrate the importance of better reference values, and we re-emphasise the need for London-dispersion corrections in density functional theory (DFT) treatments of thermochemical problems, including Minnesota methods. We assessed 217 variations of dispersion-corrected and -uncorrected density functional approximations, and carried out a detailed analysis of 83 of them to identify robust and reliable approaches. Double-hybrid functionals are the most reliable approaches for thermochemistry and noncovalent interactions, and they should be used whenever technically feasible. These are, in particular, DSD-BLYP-D3(BJ), DSD-PBEP86-D3(BJ), and B2GPPLYP-D3(BJ). The best hybrids are ωB97X-V, M052X-D3(0), and ωB97X-D3, but we also recommend PW6B95-D3(BJ) as the best conventional global hybrid. At the meta-generalised-gradient (meta-GGA) level, the SCAN-D3(BJ) method can be recommended. Other meta-GGAs are outperformed by the GGA functionals revPBE-D3(BJ), B97-D3(BJ), and OLYP-D3(BJ). We note that many popular methods, such as B3LYP, are not part of our recommendations. In fact, with our results we hope to inspire a change in the user community's perception of common DFT methods. We also encourage method developers to use GMTKN55 for cross-validation studies of new methodologies.

Entities:  

Year:  2017        PMID: 29110012     DOI: 10.1039/c7cp04913g

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  97 in total

1.  Survival of the most transferable at the top of Jacob's ladder: Defining and testing the ωB97M(2) double hybrid density functional.

Authors:  Narbe Mardirossian; Martin Head-Gordon
Journal:  J Chem Phys       Date:  2018-06-28       Impact factor: 3.488

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

Authors:  Ying Wang; Pragya Verma; Xinsheng Jin; Donald G Truhlar; Xiao He
Journal:  Proc Natl Acad Sci U S A       Date:  2018-09-20       Impact factor: 11.205

3.  High accuracy quantum-chemistry-based calculation and blind prediction of macroscopic pKa values in the context of the SAMPL6 challenge.

Authors:  Philipp Pracht; Rainer Wilcken; Anikó Udvarhelyi; Stephane Rodde; Stefan Grimme
Journal:  J Comput Aided Mol Des       Date:  2018-08-23       Impact factor: 3.686

4.  How accurate are approximate quantum chemical methods at modelling solute-solvent interactions in solvated clusters?

Authors:  Junbo Chen; Bun Chan; Yihan Shao; Junming Ho
Journal:  Phys Chem Chem Phys       Date:  2020-02-19       Impact factor: 3.676

5.  Gas-Phase Synthesis and Reactivity of Ligated Group 10 Ions in the Formal +1 Oxidation State.

Authors:  Kim Greis; Yang Yang; Allan J Canty; Richard A J O'Hair
Journal:  J Am Soc Mass Spectrom       Date:  2019-06-10       Impact factor: 3.109

6.  Doubly hybrid density functionals that correctly describe both density and energy for atoms.

Authors:  Neil Qiang Su; Zhenyu Zhu; Xin Xu
Journal:  Proc Natl Acad Sci U S A       Date:  2018-02-14       Impact factor: 11.205

7.  M06-SX screened-exchange density functional for chemistry and solid-state physics.

Authors:  Ying Wang; Pragya Verma; Lujia Zhang; Yaqi Li; Zhonghua Liu; Donald G Truhlar; Xiao He
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-17       Impact factor: 11.205

8.  A comparison of computational methodologies for the structural modelling of biologically relevant zinc complexes.

Authors:  Gökcen Savasci; Merlys Borges-Martínez; Raphael J F Berger; Christian Ochsenfeld; Raúl Mera-Adasme
Journal:  J Mol Model       Date:  2019-08-09       Impact factor: 1.810

9.  Linear correlation models for the redox potential of organic molecules in aqueous solutions.

Authors:  Jessica C Ortiz-Rodríguez; Juan A Santana; Dalvin D Méndez-Hernández
Journal:  J Mol Model       Date:  2020-03-07       Impact factor: 1.810

10.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

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