A computational tool to accurately and quickly predict<sup>19</sup>F NMR chemical shifts of molecules with fluorine–carbon and fluorine–boron bonds

Rzepa, Henry; Dumon, Alexandre S.; Alamillo‐Ferrer, Carla; Burés, Jordi; Procter, Richard; Sheppard, Tom D.; Whiting, Andrew

doi:10.1039/d2cp02317b

Cited by 6 publications

(3 citation statements)

References 150 publications

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“…NMR parameters of open-and closed-shell graphene nanoflakes were studied by Scopel et al 84 The mechanism of a-pinene hydrogenation was studied theoretically by Yang et al 85 Tautomerism of 1H-benzo[de]cinnolines and their protonated forms was studied by Alkorta et al 86 using DFT methodology. Experimental and theoretical DFT studies on proton NMR spectra and conformational exchange of S-n-alkyl-tetrahydrothiophenium cations of some ionic liquids was reported by Ha ¨gele et al 87 Surprizing magnitudes of 13 C nuclear magnetic shieldings for sp-and sp 2 -hybridized carbon atoms in graphyne systems were predicted by Lantto et al 88 A theoretical tool to accurately predict 19 F NMR chemical shifts of molecules with fluorine-carbon and fluorine-boron bonds was developed by Whiting et al 89 Uludag and Serdarog ˘lu 90 studied experimentally C-2 cyanomethylation of the indole synthesis and their conclusions were supported by theoretical calculations. 17 O NMR and DFT study of hydrogen bonding were reported by Balevic ˇius et al 91 Asakura, Kuwahara and Nakagawa 92 performed relativistic DFT calculations of NMR chemical shifts of heavy-metal nitrates in water.…”

Section: Dft Studies Of Nuclear Shieldingmentioning

confidence: 99%

Theory and computation of nuclear shielding

2023

Nuclear Magnetic Resonance

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This chapter continues earlier reviews of essential works on theoretical issues of nuclear magnetic shieldings published from 1 January to 31 December 2022. As previously, the chapter concentrates on theoretical calculations of nuclear shielding parameters and/or chemical shifts in case of free molecules in the gas phase and solution, and in some cases in the solid state using a periodic formulation. The nuclear shieldings in solution are mainly calculated using inexpensive polarizable continuum model of solvent (PCM). The use of discrete solvent molecules (in particular water and other polar solvents), which is essential for correct description of hydrogen bonding, is often omitted by numerous authors. Most calculations use a relatively simple and fast, but reliable density functional theory (DFT) combined with gauge-including atomic orbital (GIAO) approach. On the other hand, reports on prediction of nuclear magnetic shielding parameters using high level theoretical methods combined with large and flexible basis sets are not so common due to the enormous computational cost. In addition, correction of raw theoretical nuclear magnetic shieldings due to ro-vibration (zero-point vibration correction, ZPVC) and temperature corrections (TC), as well as implicit and explicit solvent effects and relativistic corrections are not often reported.

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Section: Dft Studies Of Nuclear Shieldingmentioning

confidence: 99%

Theory and computation of nuclear shielding

2023

Nuclear Magnetic Resonance

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“…This left every molecule with one shielding constant for every equivalent set of fluorines. Fluorine shielding constants were converted to chemical shifts with linear regression as in Dumon et al [8] QM calculations were performed in parallel to ML predictions, so the linear regression was fitted to the same training set as the corresponding ML method. 19 F NMR chemical shifts are fed into the ML training set.…”

Section: Quantum Mechanics Setupmentioning

confidence: 99%

“…Their performance in predicting specifically 19 F NMR chemical shifts has been increasing steadily. [8] [9] Several density functional theory (DFT) methods have shown a favorable balance of speed and accuracy, [8] especially after a correction by linear regression. [9] In this work, we will further formalize our usage of ML methods to predict 19 F NMR chemical shift and then extend it with QM methodology.…”

Section: Introductionmentioning

confidence: 99%

QM assisted ML for 19F NMR chemical shift prediction

Penner,

Vulpetti

2023

J Comput Aided Mol Des

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Ligand-observed 19 F NMR detection is an efficient method for screening libraries of fluorinated molecules in fragment-based drug design campaigns. Screening fluorinated molecules in large mixtures makes 19 F NMR a high-throughput method. Typically, these mixtures are generated from pools of well-characterized fragments. By predicting 19 F NMR chemical shift, mixtures could be generated for arbitrary fluorinated molecules facilitating for example focused screens. In a previous publication, we introduced a method to predict 19 F NMR chemical shift using rooted fluorine fingerprints and machine learning (ML) methods. Having observed that the quality of the prediction depends on similarity to the training set, we here propose to assist the prediction with quantum mechanics (QM) based methods in cases where compounds are not well covered by a training set. Beyond similarity, the performance of ML methods could be associated with individual features in compounds. A combination of both could be used as a procedure to split input data sets into those that could be predicted by ML and those that required QM processing. We could show on a proprietary fluorinated fragment library, known as LEF (Local Environment of Fluorine), and a public Enamine data set of 19 F NMR chemical shifts that ML and QM methods could synergize to outperform either method individually. Models built on Enamine data, as well as model building and QM workflow tools, can be found at https://github.com/PatrickPenner/lefshift and https://github.com/PatrickPenner/lefqm.

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Direct Synthesis of Fluorinated Carbon Materials via a Solid‐State Mechanochemical Reaction Between Graphite and PTFE

Jang

Zhao

Baek

et al. 2023

Adv Funct Materials

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Fluorinated carbon materials (FCMs) have received significant attention, because of their exceptional stability, which is associated with the strong C‐F bonding, the strongest among carbon single bonds. However, the fluorination of carbon materials requires extremely toxic and moisture‐sensitive reagents, which makes it inapplicable for practical uses. Here, a straightforward and relatively safe method are reported for the scalable synthesis of FCMs, by mechanochemical depolymerization of polytetrafluoroethylene (PTFE) and fragmentation of graphite. The resultant FCMs are evaluated as anode materials for lithium‐ion batteries (LIBs). An optimized FCM delivered capacities as high as 951.6 and 329.3 mAh g −1 at 0.05 and 10 A g −1, respectively. It also demonstrated capacity retention as high as 76.6% even after 1000 cycles at 2.0 A g −1.

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A computational tool to accurately and quickly predict¹⁹F NMR chemical shifts of molecules with fluorine–carbon and fluorine–boron bonds

Abstract: We report the evaluation of density-functional-theory (DFT) based procedures for predicting 19F NMR chemical shifts at modest computational cost for a range of molecules with fluorine bonds, to be used...

Cited by 6 publications

References 150 publications

Theory and computation of nuclear shielding

Theory and computation of nuclear shielding

QM assisted ML for 19F NMR chemical shift prediction

Direct Synthesis of Fluorinated Carbon Materials via a Solid‐State Mechanochemical Reaction Between Graphite and PTFE

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A computational tool to accurately and quickly predict19F NMR chemical shifts of molecules with fluorine–carbon and fluorine–boron bonds

Abstract: We report the evaluation of density-functional-theory (DFT) based procedures for predicting 19F NMR chemical shifts at modest computational cost for a range of molecules with fluorine bonds, to be used...

Cited by 6 publications

References 150 publications

Theory and computation of nuclear shielding

Theory and computation of nuclear shielding

QM assisted ML for 19F NMR chemical shift prediction

Direct Synthesis of Fluorinated Carbon Materials via a Solid‐State Mechanochemical Reaction Between Graphite and PTFE

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Product

Resources

About

A computational tool to accurately and quickly predict¹⁹F NMR chemical shifts of molecules with fluorine–carbon and fluorine–boron bonds