NMR Chemical Shifts. 1. The Role of Relative Atomic Orbital Phase in Determining the Sign of the Paramagnetic Terms:  ClF, CH3F, CH3NH3+, FNH3+, and HC⋮CF

1998 ◽  
Vol 102 (45) ◽  
pp. 8766-8773 ◽  
Author(s):  
Kenneth B. Wiberg ◽  
Jack D. Hammer ◽  
Kurt W. Zilm ◽  
James R. Cheeseman ◽  
Todd A. Keith
Keyword(s):  
Chemical Shifts ◽  
Atomic Orbital ◽  
Nmr Chemical Shifts ◽  
Orbital Phase

Ab initio calculation of phosphorus NMR chemical shifts in the gauge including atomic orbital method

1991 ◽  
Vol 157 (1-2) ◽  
pp. 105-110 ◽  
Author(s):  
D.B. Chesnut ◽  
B.E. Rusiloski
Keyword(s):  
Ab Initio ◽  
Ab Initio Calculation ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Orbital Method ◽  
Phosphorus Nmr ◽  
Nmr Chemical Shifts

Understanding the NMR chemical shifts for 6-halopurines: role of structure, solvent and relativistic effects

2010 ◽  
Vol 12 (19) ◽  
pp. 5126 ◽  
Author(s):  
Stanislav Standara ◽  
Kateřina Maliňáková ◽  
Radek Marek ◽  
Jaromír Marek ◽  
Michal Hocek ◽  
...  
Keyword(s):  
Chemical Shifts ◽  
Relativistic Effects ◽  
Nmr Chemical Shifts

scholarly journals The role of explicit solvent molecules in the calculation of NMR chemical shifts of glycine in water

2018 ◽  
Vol 137 (7) ◽  
Author(s):  
María C. Caputo ◽  
Patricio F. Provasi ◽  
Stephan P. A. Sauer
Keyword(s):  
Chemical Shifts ◽  
Nmr Chemical Shifts ◽  
Explicit Solvent ◽  
Solvent Molecules

SYNTHESIS AND GIAO NMR CALCULATIONS FOR TWO DIASTEREOISOMERS OF 2′-ACETYLOXY-2′-PHENYLSPIRO[INDENO[1,2-b]QUINOXALIN-11,1′-CYCLOPROPANE]

2012 ◽  
Vol 11 (06) ◽  
pp. 1227-1236 ◽  
Author(s):  
MASOUD SHAABANZADEH ◽  
HAMID HASHEMIMOGHADDAM ◽  
MARYAM BIKHOF TORBATI ◽  
TAHEREH SOLEYMANI AHOEE
Keyword(s):  
Nmr Spectra ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Basis Sets ◽  
Computational Results ◽  
Nmr Chemical Shifts ◽  
H Nmr ◽  
Chemical Structures ◽  
B3lyp Method ◽  
Experimental Values

Two diastereoisomers of 2′-acetyloxy-2′-phenylspiro[indeno[1,2-b]quinoxalin-11,1′-cyclopropane] were synthesized and their 1 H NMR spectra were recorded. Their chemical structures were fully optimized at B3LYP/6-311+G(d,p) level of theory using the Gaussian 03W program package. The 1 H NMR chemical shifts were calculated for geometry-optimized structures of the diastereoisomers with the gauge independent atomic orbital (GIAO) and B3LYP method with the 6-311+G(d,p), 6-311++G(d), 6-31++G(d,p) and 6-31+G(d) basis sets. The computational results were then compared with the experimental values and the structures associated with each spectrum were assigned.

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 Quantum chemical studies on structural, spectroscopic, nonlinear optical, and thermodynamic properties of the 1,2,4-triazole compound

2021 ◽  
Vol 27 (1) ◽  
pp. 112-132
Author(s):  
Hilal Medetalibeyoğlu ◽  
Haydar Yüksek
Keyword(s):  
Density Functional Theory ◽  
Molecular Orbital ◽  
Density Functional ◽  
Chemical Shifts ◽  
Atomic Orbital ◽  
Fluorescence Emission ◽  
Strong Relationship ◽  
Basis Set ◽  
Functional Theory ◽  
Nmr Chemical Shifts

Abstract In this study, the structure of 4-[4-(diethylamino)-benzylideneamino]-5-benzyl-2H-1,2,4-triazol-3(4H)-one (DBT) was examined through spectroscopic and theoretical analyses. In this respect, the geometrical, vibrational frequency, 1H and 13C-nuclear magnetic resonance (NMR) chemical shifts, thermodynamic, hyperpolarizability, and electronic properties including the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) energies of DBT as a potential non-linear optical (NLO) material were investigated using density functional theory at the B3LYP level with the 6-311G basis set. 1H and 13C-NMR chemical shifts of DBT with the gauge-invariant atomic orbital and continuous set of gauge transformation methods (in the solvents) were estimated, and the computed chemical shift values displayed excellent alignment with observed ones. Time-dependent density-functional theory (TD-DFT) calculations with the integral equation formalism polarizable continuum model within various solvents and gas phases in the ground state were used to evaluate UV-vis absorption and fluorescence emission wavelengths. Thermodynamic parameters including enthalpy, heat capacity, and entropy for DBT were also calculated at various temperatures. Moreover, calculations of the NLO were carried out to obtain the title compound’s electric dipole moment and polarizability properties. To illustrate the effect of the theoretical method on the spectroscopic and structural properties of DBT, experimental data of structural and spectroscopic parameters were used. The correlational analysis results were observed to indicate a strong relationship between the experimental and theoretical results.

High-Frequency13C and29Si NMR Chemical Shifts in Diamagnetic Low-Valence Compounds of TlIand PbII: Decisive Role of Relativistic Effects

2016 ◽  
Vol 55 (4) ◽  
pp. 1770-1781 ◽  
Author(s):  
Jan Vícha ◽  
Radek Marek ◽  
Michal Straka
Keyword(s):  
Chemical Shifts ◽  
Relativistic Effects ◽  
Decisive Role ◽  
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>

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