Evidence map›Paper›PMID 31834497›Full record

ArticleJournal of molecular modeling2019

How accurate is density functional theory in predicting spin density? An insight from the prediction of hyperfine coupling constants.

Maciej Witwicki, Paulina K Walencik, Julia Jezierska

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Article in Journal of molecular modeling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Electric Field Dependence of EPR Hyperfine Coupling Constants.The journal of physical chemistry. A · 2024
    Article
  4. Article
  5. Formation and structural features of micelles formed by surfactin homologues.Frontiers in bioengineering and biotechnology · 2023
    Article
  6. Article
  7. Antiradical Properties of N-Oxide Surfactants-Two in One.International journal of molecular sciences · 2021
    Article
  8. Article
  9. 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

3 authors.

Maciej WitwickiFaculty of Chemistry, Wrocław University, ul. F. Joliot-Curie 14, 50-383, Wrocław, Poland. maciej.witwicki@chem.uni.wroc.pl.ORCID http://orcid.org/0000-0002-9347-2538
Paulina K WalencikFaculty of Chemistry, Wrocław University, ul. F. Joliot-Curie 14, 50-383, Wrocław, Poland.ORCID http://orcid.org/0000-0001-5080-9105
Julia JezierskaFaculty of Chemistry, Wrocław University, ul. F. Joliot-Curie 14, 50-383, Wrocław, Poland.ORCID http://orcid.org/0000-0001-8776-3454

Funding

Narodowe Centrum Nauki 2018/02/X/ST5/01186
6 · The paper itself

Abstract

Electron paramagnetic resonance (EPR) spectroscopy has been proven to be an important technique for studying paramagnetic systems. Probably, the most accessible EPR parameter and the one that provides a significant amount of information about molecular structure and spin density is the hyperfine coupling constant (HFCC). Hence, accurate quantum-chemical modeling of HFCCs is frequently essential to the adequate interpretation of EPR spectra. It requires the precise spin density, which is the difference between the densities of α- and β-electrons, and thus, its quality is expected to reflect the quality of the total electron density. The question of which approximate exchange-correlation density functional yields sufficiently accurate HFCCs, and thus, the spin density remains open. To assess the performance of well-established density functionals for calculating HFCCs, we used a series of 26 small paramagnetic species and compared the obtained results to the CCSD reference values. The performance of DFT was also tested on EPR-studied o-semiquinone radical interacting with water molecules and Mg

Indexed as

DFTDLPNO-CCSDHyperfine coupling constantRadicalsSemiquinoneSpin density

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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.