Towards an Accurate Prediction of Nitrogen Chemical Shifts by Density Functional Theory and Gauge‐Including Atomic Orbital

Gao, Peng; Wang, Xingyong; Yu, Haibo

doi:10.1002/adts.201800148

Cited by 16 publications

(53 citation statements)

References 30 publications

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“…26,28−30 In particular, the important advantage of method 5 (with the SMD solvent model) is that it could provide chemical shift predictions for 1 H, 13 C, 15 N, and 11 B with a consistent accuracy in one set of calculation via the corresponding changes of the applied solvents. 27,29…”

Section: Results and Discussionmentioning

confidence: 99%

“…53 The first six levels of theory (Table 1, method 1–6) are to follow Tantillo and co-workers’ work on 1 H and 13 C chemical shifts 26,27 and our previous study on 15 N chemical shifts. 29 In addition, two methods (Table 1, method 7 and 8), which have been shown to provide accurate predictions for both 15 N 28 and 13 C 30 chemical shift calculations, were also included for comparison. To further investigate the solvent effects, additional calculations with an implicit solvent model included in the geometry optimization step were carried out for two levels of theory (method 5′ and 7′).…”

Section: Methodsmentioning

confidence: 99%

“…Through these two scaling factors, the calculated isotropic shielding constants can be converted into chemical shifts with sufficient accuracy, for instance, to allow distinguishing between different stereoisomers. 29…”

Section: Introductionmentioning

confidence: 99%

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¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

Gao¹,

Wang²,

Huang

et al. 2019

ACS Omega

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11 B nuclear magnetic resonance (NMR) spectroscopy is a useful tool for studies of boron-containing compounds in terms of structural analysis and reaction kinetics monitoring. A computational protocol, which is aimed at an accurate prediction of 11 B NMR chemical shifts via linear regression, was proposed based on the density functional theory and the gauge-including atomic orbital approach. Similar to the procedure used for carbon, hydrogen, and nitrogen chemical shift predictions, a database of boron-containing molecules was first compiled. Scaling factors for the linear regression between calculated isotropic shielding constants and experimental chemical shifts were then fitted using eight different levels of theory with both the solvation model based on density and conductor-like polarizable continuum model solvent models. The best method with the two solvent models yields a root-mean-square deviation of about 3.40 and 3.37 ppm, respectively. To explore the capabilities and potential limitations of the developed protocols, classical boron–hydrogen compounds and molecules with representative boron bonding environments were chosen as test cases, and the consistency between experimental values and theoretical predictions was demonstrated.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

Gao¹,

Wang²,

Huang

et al. 2019

ACS Omega

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“…Such a correction has also been successfully applied for 11 B, 1 H, 13 C, and 15 N chemical shift predictions in previous studies. [ 1,28,30,31 ]…”

Section: Resultsmentioning

confidence: 99%

A systematic benchmarking of ³¹P and ¹⁹F NMR chemical shift predictions using different DFT/GIAO methods and applying linear regression to improve the prediction accuracy

Gao

Zhang

Chen

2020

Int J of Quantum Chemistry

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A systematic benchmark study of phosphorus and fluorine nuclear magnetic resonance chemical shift predictions using six different density functional theory (DFT)/the gauge‐including atomic orbital (GIAO) methods was conducted. Two databases were compiled: one consists of 35 phosphorus‐containing molecules, which cover the most common intramolecular bonding environments of trivalent and pentavalent phosphorus atoms; the other is composed of 46 fluorine‐containing molecules. The characteristics of each DFT/GIAO method with different solvent models were demonstrated in detail. The application of linear regression between the calculated isotropic shielding constants and experimental chemical shifts was applicable to improving the prediction accuracy. The best methods with the solvation model based on density (SMD) and conductor‐like polarizable continuum model (CPCM) implicit solvent models for 31P chemical shifts predictions are able to yield a root‐mean‐square deviation of 5.58 and 5.42 ppm, respectively; for 19F, the corresponding lowest prediction errors with these two applied solvent models are 4.43 and 4.12 ppm, respectively. The developed scaling factors fitted from linear regression are applicable to enhancing the chance of successful structural elucidations of phosphorus or fluorine‐containing compounds as an efficient complement to 13C, 1H, 11B, and 15N chemical shift predictions.

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“…In recent years, linear regression (LR) 3 has been used for the correction of chemical shift predictions, complementing the aforementioned DFT and GIAO methods [7][8][9] . The LR between calculated isotropic shielding constants () and experimental chemical shifts () is expressed in a simple form:  = slope×+intercept, where "slope" and "intercept" are fitted with respect to a considerable amount of data.…”

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confidence: 99%

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

Gao¹,

Peng³

et al. 2019

Preprint

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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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Towards an Accurate Prediction of Nitrogen Chemical Shifts by Density Functional Theory and Gauge‐Including Atomic Orbital

Cited by 16 publications

References 30 publications

¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

A systematic benchmarking of ³¹P and ¹⁹F NMR chemical shift predictions using different DFT/GIAO methods and applying linear regression to improve the prediction accuracy

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

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Towards an Accurate Prediction of Nitrogen Chemical Shifts by Density Functional Theory and Gauge‐Including Atomic Orbital

Cited by 16 publications

References 30 publications

11B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

11B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

A systematic benchmarking of 31P and 19F NMR chemical shift predictions using different DFT/GIAO methods and applying linear regression to improve the prediction accuracy

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

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¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

¹¹B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules

A systematic benchmarking of ³¹P and ¹⁹F NMR chemical shift predictions using different DFT/GIAO methods and applying linear regression to improve the prediction accuracy