Evidence map›Paper›PMID 40099631›Full record

ArticleJournal of computational chemistry2025

Optimizing Computational Parameters for Nuclear Electronic Orbital Density Functional Theory: A Benchmark Study on Proton Affinities.

Raza Ullah Khan, Ralf Tonner-Zech

Abstract read
In one paragraph

Article in Journal of computational chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Multicomponent Double-Hybrid Density Functional Theory.Journal of chemical theory and computation · 2025
    Article
  3. Reliable pJournal of the American Chemical Society · 2025
    Article
  4. Article
  5. Article
  6. 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

2 authors.

Raza Ullah KhanWilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Leipzig, Germany.ORCID https://orcid.org/0000-0002-6759-8559
Ralf Tonner-ZechWilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Leipzig, Germany.ORCID https://orcid.org/0000-0001-9480-7187

Funding

Deutsche Forschungsgemeinschaft 443871192
6 · The paper itself

Abstract

This study benchmarks the nuclear electronic orbital density functional theory (NEO-DFT) method for a set of molecules that is larger than in previous studies. The focus is on proton affinity predictions to assess the influences of computational parameters. NEO-DFT incorporates nuclear quantum effects for protons involved in protonation processes. Using a test set of 72 molecules with experimental proton affinities as reference, we evaluated various exchange-correlation functionals, finding that B3LYP-based functionals deliver the most accurate results. Among the tested functionals, CAM-B3LYP performs the best with an MAD value of 6.2 kJ/mol with respect to experimental data. In NEO-DFT, electron-proton correlation (epc) functionals were assessed, with LDA-type epc17-2 yielding comparable results to the GGA-type epc19 functional. Compared to traditional DFT (MAD value of 31.6 kJ/mol), which treats nuclei classically, NEO-DFT provides enhanced accuracy for proton affinities when electron-proton correlation is included. Regarding basis sets, the def2-QZVP electronic basis set achieved the highest accuracy with an MAD value of 5.0 kJ/mol, though at a higher computational cost compared to def2-TZVP and def2-SVP, while nuclear basis sets showed minimal impact on proton affinity accuracy and no consistent trend. Overall, this study demonstrates NEO-DFT's efficacy in addressing nuclear quantum effects for proton affinity predictions, providing guidance on optimal parameter selection for future NEO-DFT applications.

Indexed as

benchmarkingcomputational parameterslight nucleiNEO‐DFTnuclear‐electronic orbitalnuclear quantum effectsproton affinity

Identifiers

PMID40099631
PMCPMC11915487

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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.