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

Physical Chemistry Chemical Physics · 2017 · Vol. 19(48) · pp. 32184–32215
Lars GoerigkAndreas HansenChristoph BauerStephan EhrlichAsim NajibiStefan 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.

Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesThermal and Kinetic AnalysisThermochemistryDensity functional theoryBenchmark (surveying)Non-covalent interactionsDispersion (optics)ChemistryComputer scienceComputational chemistryThermodynamicsDatabase

Funding

  • National Computational Infrastructure
  • Australian Government
  • Deutsche Forschungsgemeinschaft
  • Gottfried Wilhelm Leibniz Universität Hannover
  • Australian Research Council
  • Leibniz-Gemeinschaft
  • National Cancer Institute
Citations
1,858
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225
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