A machine learning approach for predicting the fluorination strength of electrophilic fluorinating reagents

Saini, Vaneet

doi:10.1039/d2cp03281c

Cited by 6 publications

(1 citation statement)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reaction that was studied involved the direct electrophilic uorination of alkenes to form aryl allyl uorides in a fast and e cient manner, without the need of any transition metal catalyst. [24] The reaction employed styrenes as ole n source, Select uor [25] as electrophilic uorinating reagent and DMF as a solvent that furnished the allyl uoride products in good to excellent yields. Lately, a similar reaction on acyclic alkenes have also been reported.…”

Section: Resultsmentioning

confidence: 99%

A mechanistic investigation of metal-free allylic fluorination of styrenes for the synthesis of allyl fluoride derivatives using density functional theory

Singh,

Saini

2023

Struct Chem

Self Cite

View full text Add to dashboard Cite

The primary objective of this work is to delve into the intricacies of allylic uorination reactions through the application of density functional theory (DFT) calculations. These reactions hold signi cant importance in the realm of synthesizing organo uorine compounds. The speci c focus lies on comprehending the interaction mechanisms when styrenes, a class of organic molecules, come in contact with an electrophilic uorinating reagent known as Select uor. Notably, this interaction pathway demonstrates remarkable e ciency in yielding allylic uoride products. The proposed mechanism for this transformation involves a sequential process. To unveil the microcosmic intricacies governing this reaction between the alkene substrate and Select uor, advanced computational methodologies are employed. The paper systematically outlines the computational strategies harnessed to probe the minute details of the reaction mechanism. The outcomes of these computations are subsequently subjected to thorough analysis, encompassing crucial facets such as transition states and energy barriers. This analytical depth enhances the fundamental understanding of the reaction mechanism and sheds light on the underlying factors in uencing its feasibility and e ciency.In a broader context, the insights garnered from this study carry signi cant utility. They provide pivotal guidance for the optimization of reaction conditions, facilitating the ne-tuning of experimental setups. Moreover, the elucidated mechanism serves as a platform for the design of even more e cient and selective allylic uorination reactions. This paper, by amalgamating theoretical insights with practical synthetic objectives, contributes to the broader advancement of organo uorine compound synthesis and allied elds.

show abstract

Section: Resultsmentioning

confidence: 99%

A mechanistic investigation of metal-free allylic fluorination of styrenes for the synthesis of allyl fluoride derivatives using density functional theory

Singh,

Saini

2023

Struct Chem

Self Cite

View full text Add to dashboard Cite

show abstract

A machine learning approach for predicting the reactivity power of hypervalent iodine compounds

Saini,

Kataria,

Rajput

2024

Artificial Intelligence Chemistry

View full text Add to dashboard Cite

Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives

Saini,

Kumar

2024

Artificial Intelligence Chemistry

View full text Add to dashboard Cite

A machine learning approach for predicting the fluorination strength of electrophilic fluorinating reagents

Abstract: The unusual properties of a wide diversity of organofluorine compounds have provided strong incentives to the scientific community for the development of this field. Parallel to the constantly growing number...

Cited by 6 publications

References 58 publications

A mechanistic investigation of metal-free allylic fluorination of styrenes for the synthesis of allyl fluoride derivatives using density functional theory

A mechanistic investigation of metal-free allylic fluorination of styrenes for the synthesis of allyl fluoride derivatives using density functional theory

A machine learning approach for predicting the reactivity power of hypervalent iodine compounds

Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives

Contact Info

Product

Resources

About