DELTA50: A Highly Accurate Database of Experimental 1H and 13C NMR Chemical Shifts Applied to DFT Benchmarking

Abstract

Density functional theory (DFT) benchmark studies of 1H and 13C NMR chemical shifts often yield differing conclusions, likely due to non-optimal test molecules and non-standardized data acquisition. To address this issue, we carefully selected and measured 1H and 13C NMR chemical shifts for 50 structurally diverse small organic molecules containing atoms from only the first two rows of the periodic table. Our NMR dataset, DELTA50, was used to calculate linear scaling factors and to evaluate the accuracy of 73 density functionals, 40 basis sets, 3 solvent models, and 3 gauge-referencing schemes. The best performing DFT methodologies for 1H and 13C NMR chemical shift predictions were WP04/6-311++G(2d,p) and ωB97X-D/def2-SVP, respectively, when combined with the polarizable continuum solvent model (PCM) and gauge-independent atomic orbital (GIAO) method. Geometries should be optimized at the B3LYP-D3/6-311G(d,p) level including the PCM solvent model for the best accuracy. Predictions of 20 organic compounds and natural products from a separate probe set had root-mean-square deviations (RMSD) of 0.07 to 0.19 for 1H and 0.5 to 2.9 for 13C. Maximum deviations were less than 0.5 and 6.5 ppm for 1H and 13C, respectively.

Reactions of 5-(Trialkyl)silylpent-1-en-4-yn-3-ones with Hydrazines: Original Synthetic Routes to Luminescent Substances Containing Azole Motifs

“…54 Chemical shifts ( 1 H) were normalized to a standard Me 4 Si, according to the equation: ( 1 H) = [32.265 -( 1 H)]/1.0311. 41 Starting conjugated enynones 1a-f were obtained by the oxidation of corresponding vinyl ethynyl carbinols by -MnO 2 in CH 2 Cl 2 . 8c, 31 The rest of enynones 1g-o were obtained by the interaction of bis(trimethylsilyl)acetylene with acid chlorides of ,-unsaturated acids in the presence of AlCl 3 , according to the method of Birkofer.…”

“…Chemical shifts values (2.20, 2.97 ppm for trans-conformer and 2.68, 2.93 for cis-B, respectively) are highly correlated with the calculations data (see Scheme 2). 41 According to HSQCED spectrum, these protons generate carbon atom signals, one for each pair (at 42.39 ppm for cis-B and 55.71 for trans-B). The DQF-COSY spectrum has shown the interaction of protons of the CH 2 groups (cis-B and trans-B) with triplets at 4.33 and 4.75 ppm, which correspond to the signals of carbon atoms at 63.37 and 69.20 ppm, respectively (by HSQCED).…”

Using atomic clustering based on structural and electronic descriptors that consider surrounding environment to evaluate local properties of DFT functionals

“…This is useful when the reference molecules have properties similar to those of the target molecule. Another approach involves using benchmark datasets constructed from various viewpoints, namely interaction type (e.g., non-covalent bonding, [3][4][5][6][7][8][9][10][11][12][13][14][15][16] halogen bonding, 17 intermolecular interactions, [18][19][20] and water interactions [21][22][23] ) and molecular properties (e.g., bond-dissociation energies, 24,25 rotational constants, 26 chalcogen bonding, 27 conformation energies, [28][29][30] ionization potentials, 31 nuclear magnetic resonance [NMR] chemical shifts, [32][33][34][35] heats of sublimation, 36 electron coupling values, 37 carbonation, 38 reaction barriers, [39][40][41][42][43][44] reaction energies, [42][43][44][45] etc....…”

“…Atomic information that considers the surrounding environment of a molecule can be used to obtain further insight into the accuracies of DFT functionals. For example, the accuracies of 13 C NMR chemical-shift calculated using DFT functionals have been evaluated from the viewpoint of the C-atom chemical species, including those involving sp 3 , sp 2 , and sp hybridization, 34 In this study, we developed a classification method based on machine-learning clustering combined with structural and electronic descriptors to reveal errors in calculations using the DFT functional at a level interpretable by humans. By linking these errors to chemical knowledge, suitable DFT functionals can be selected based on the properties and functional groups of interest.…”

“…Atomic information that considers the surrounding environment of a molecule can be used to obtain further insight into the accuracies of DFT functionals. For example, the accuracies of 13 C NMR chemical‐shift calculated using DFT functionals have been evaluated from the viewpoint of the C‐atom chemical species, including those involving sp 3 , sp 2 , and sp hybridization, 34 with the results providing different perspectives. Recent developments in machine learning have enabled the chemical nature of each atom in a molecule to be classified in a manner beyond what is possible using human intuition.…”