Evidence map›Paper›PMID 35696652›Full record

ArticleChemphyschem : a European journal of chemical physics and physical chemistry2022

Efficient and Accurate Description of Diels-Alder Reactions Using Density Functional Theory.

Daniele Loco, Isabelle Chataigner, Jean-Philip Piquemal, Riccardo Spezia

Abstract read
In one paragraph

Article in Chemphyschem : a European journal of chemical physics and physical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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  7. Efficient and Accurate Description of Diels-Alder Reactions Using Density Functional Theory.Chemphyschem : a European journal of chemical physics and physical chemistry · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniele LocoSorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 4 Place Jussieu, 75005, Paris, France.
Isabelle ChataignerSorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 4 Place Jussieu, 75005, Paris, France.
Jean-Philip PiquemalSorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 4 Place Jussieu, 75005, Paris, France.
Riccardo SpeziaSorbonne Université, Laboratoire de Chimie Théorique, UMR 7616 CNRS, 4 Place Jussieu, 75005, Paris, France.ORCID 0000-0001-5160-489X

Funding

European Research Council (ERC)European Union's Horizon 2020 research and innovation program 810367European Union's Horizon 2020 research and innovation program EMC2HORIZON EUROPE European Research Council 810367
6 · The paper itself

Abstract

Modeling chemical reactions using Quantum Chemistry is a widely used predictive strategy capable to complement experiments in order to understand the intrinsic mechanisms guiding the chemicals towards the most favorable reaction products. However, at this purpose, it is mandatory to use reliable and computationally tractable theoretical methods. In this work, we focus on six Diels-Alder reactions of increasing complexity and perform an extensive benchmark of middle- to low-cost computational approaches to predict the characteristic reactions energy barriers. We found that Density Functional Theory, using the ωB97XD, LC-ωPBE, CAM-B3LYP, M11 and MN12SX functionals, with empirical dispersion corrections coupled to an affordable 6-31G basis set, provides quality results for this class of reactions, at a small computational effort. Such efficient and reliable simulation protocol opens perspectives for hybrid QM/MM molecular dynamics simulations of Diels-Alder reactions including explicit solvation.

Indexed as

Molecular Dynamics SimulationQuantum TheoryDensity Functional Theorycycloadditiondensity functional calculationsdiels-aldertransition states

Identifiers

PMID35696652
PMCPMC9796631

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.