Evidence map›Paper›PMID 32055318›Full record

ArticleChemical science2019

Electron density learning of non-covalent systems.

Alberto Fabrizio, Andrea Grisafi, Benjamin Meyer, Michele Ceriotti, Clemence Corminboeuf

Abstract read
In one paragraph

Article in Chemical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
  2. Quantum Mechanical/Molecular Mechanical (QM/MM) Studies of Enzymes.Advances in experimental medicine and biology · 2026
    Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. 3DReact: Geometric Deep Learning for Chemical Reactions.Journal of chemical information and modeling · 2024
    Article
  8. SPAJournal of chemical theory and computation · 2024
    Article
  9. Article
  10. Physics-Inspired Equivariant Descriptors of Nonbonded Interactions.The journal of physical chemistry letters · 2023
    Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Informing geometric deep learning with electronic interactions to accelerate quantum chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  16. Article
  17. Article
  18. Article
  19. Learning Electron Densities in the Condensed Phase.Journal of chemical theory and computation · 2021
    Article
  20. 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

5 authors.

Alberto FabrizioLaboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.
Andrea GrisafiLaboratory of Computational Science and Modeling , IMX , École Polytechnique Fédérale de Lausanne , 1015 Lausanne , Switzerland.
Benjamin MeyerLaboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.
Michele CeriottiLaboratory of Computational Science and Modeling , IMX , École Polytechnique Fédérale de Lausanne , 1015 Lausanne , Switzerland.ORCID 0000-0003-2571-2832
Clemence CorminboeufLaboratory for Computational Molecular Design , Institute of Chemical Sciences and Engineering , École Polytechnique Fédérale de Lausanne , CH-1015 Lausanne , Switzerland . Email: clemence.corminboeuf@epfl.ch.ORCID 0000-0001-7993-2879

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density

Identifiers

PMID32055318
PMCPMC6991182

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.