scholarly journals Halogen Bond of Halonium Ions: Benchmarking DFT Methods for the Description of NMR Chemical Shifts

2020 ◽  
Vol 16 (12) ◽  
pp. 7690-7701
Author(s):  
Daniel Sethio ◽  
Gerardo Raggi ◽  
Roland Lindh ◽  
Máté Erdélyi
Keyword(s):  
Halogen Bond ◽  
Chemical Shifts ◽  
Dft Methods ◽  
Nmr Chemical Shifts

Origin of the 29Si NMR chemical shift in R3Si–X and relationship to the formation of silylium (R3Si+) ions

2020 ◽  
Vol 49 (45) ◽  
pp. 16453-16463 ◽  
Author(s):  
Winn Huynh ◽  
Matthew P. Conley
Keyword(s):  
Chemical Shift ◽  
Chemical Shifts ◽  
29Si Nmr ◽  
Surface Oxygen ◽  
Nmr Chemical Shift ◽  
Dft Methods ◽  
Nmr Chemical Shifts

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.

scholarly journals Inside Cover: Natural Abundance 15 N and 13 C Solid-State NMR Chemical Shifts: High Sensitivity Probes of the Halogen Bond Geometry (Chem. Eur. J. 47/2016)

2016 ◽  
Vol 22 (47) ◽  
pp. 16694-16694 ◽  
Author(s):  
Paolo Cerreia Vioglio ◽  
Luca Catalano ◽  
Vera Vasylyeva ◽  
Carlo Nervi ◽  
Michele R. Chierotti ◽  
...  
Keyword(s):  
Solid State ◽  
Solid State Nmr ◽  
Halogen Bond ◽  
Chemical Shifts ◽  
High Sensitivity ◽  
Natural Abundance ◽  
Nmr Chemical Shifts

scholarly journals A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning

2019 ◽  
Author(s):  
Peng Gao ◽  
Jun Zhang ◽  
Qian Peng ◽  
Vassiliki-Alexandra Glezakou
Keyword(s):  
Machine Learning ◽  
1H Nmr ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Computational Cost ◽  
Experimental Studies ◽  
Dft Methods ◽  
Nmr Chemical Shifts

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>

scholarly journals Natural Abundance15N and13C Solid-State NMR Chemical Shifts: High Sensitivity Probes of the Halogen Bond Geometry

2016 ◽  
Vol 22 (47) ◽  
pp. 16819-16828 ◽  
Author(s):  
Paolo Cerreia Vioglio ◽  
Luca Catalano ◽  
Vera Vasylyeva ◽  
Carlo Nervi ◽  
Michele R. Chierotti ◽  
...  
Keyword(s):  
Solid State ◽  
Solid State Nmr ◽  
Halogen Bond ◽  
Chemical Shifts ◽  
High Sensitivity ◽  
Nmr Chemical Shifts

scholarly journals A successful DFT calculation of carbon-13 NMR chemical shifts and carbon–fluorine spin–spin coupling constants in (η6-fluoroarene)tricarbonylchromium complexes

RSC Advances ◽  
2014 ◽  
Vol 4 (52) ◽  
pp. 27290-27296 ◽  
Author(s):  
Adam Gryff-Keller ◽  
Przemysław Szczeciński
Keyword(s):  
Dft Calculation ◽  
Chemical Shifts ◽  
Coupling Constants ◽  
Spin Coupling ◽  
Dft Methods ◽  
Nmr Chemical Shifts ◽  
Shielding Constants ◽  
Spin Coupling Constants ◽  
Spin Spin Coupling ◽  
Tricarbonylchromium Complexes

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.

scholarly journals A General Protocol for the Accurate Predictions of Molecular 13C/1H NMR Chemical Shifts via Machine Learning

2019 ◽  
Author(s):  
Peng Gao ◽  
Jun Zhang ◽  
Qian Peng ◽  
Vassiliki-Alexandra Glezakou
Keyword(s):  
Machine Learning ◽  
1H Nmr ◽  
Density Functional ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Computational Cost ◽  
Experimental Studies ◽  
Dft Methods ◽  
Nmr Chemical Shifts

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>

Relativistic and Dynamic effects are needed to correctly model the 1H NMR Chemical Shifts of an Iridium Polyhydride Cluster

2020 ◽  
Author(s):  
Abril C. Castro ◽  
David Balcells ◽  
Michal Repisky ◽  
Trygve Helgaker ◽  
Michele Cascella
Keyword(s):  
1H Nmr ◽  
Chemical Shifts ◽  
Dynamic Effects ◽  
Nmr Chemical Shifts
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