Carbon-13 isotropic shielding constants and C–F spin–spin coupling constants for fluorobenzene, 3- and 4-dimethylaminofluorobenzene as well as for their tricarbonylchromium complexes have been calculated using DFT methods.
The origin in deshielding of 29Si NMR chemical shifts in R3Si–X, where X = H, OMe, Cl, OTf, [CH6B11X6], toluene, and OX (OX = surface oxygen), as well as iPr3Si+ and Mes3Si+ were studied using DFT methods.
Using 63Cu and 65Cu FT NMR, chemical shifts and large indirect spin-spin-coupling constants with phosphorus were measured in dissolved copper (I) compounds. The nuclear magnetic shielding constants derived from the chemical shifts were given in the atomic reference scale. No isotope effect for spin-spin-coupling constants was found.
A recently developed methodology for calculating, analyzing, and visualizing nuclear magnetic shielding densities is used for studying spatial contributions including ring-current contributions to 1H nuclear magnetic resonance (NMR) chemical shifts of aromatic and anti-aromatic free-base porphyrinoids. Our approach allows a visual inspection of the spatial origin of the positive (shielding) and negative (deshielding) contributions to the nuclear magnetic shielding constants. Diatropic and paratropic current-density fluxes yield both shielding and deshielding contributions implying that not merely the tropicity of the current density determines whether the contribution has a shielding or deshielding character. Instead the shielding or deshielding contribution is determined by the direction of the current-density flux with respect to the studied nucleus.
Accurate prediction of NMR chemical shifts with affordable computational cost is of great importance for rigorous structural assignments of experimental studies. However, the most popular computational schemes for NMR calculation—based on density functional theory (DFT) and gauge-including atomic orbital (GIAO) methods—still suffer from ambiguities in structural assignments. Using state-of-the-art machine learning (ML) techniques, we have developed a DFT+ML model that is capable of predicting 13C/1H NMR chemical shifts of organic molecules with high accuracy. The input for this generalizable DFT+ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT+ML model was trained with a dataset containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root-mean-square-derivations (RMSDs) for the errors between predicted and experimental 13C/1H chemical shifts are as small as 2.10/0.18 ppm, which is much lower than the typical DFT (5.54/0.25 ppm), or DFT+linear regression (4.77/0.23 ppm) approaches. It also has smaller RMSDs and maximum absolute errors than two previously reported NMR-predicting ML models. We test the robustness of the model on two classes of organic molecules (TIC10 and hyacinthacines), where we unambiguously assigned the correct isomers to the experimental ones. This DFT+ML model is a promising way of predicting NMR chemical shifts and can be easily adapted to calculated shifts for any chemical compound.<br>
A successful DFT calculation of carbon-13 NMR chemical shifts and carbon–fluorine spin–spin coupling constants in (η6-fluoroarene)tricarbonylchromium complexes