Evidence map›Paper›PMID 41170155›Full record

ArticlePrecision chemistry2025

Nanotube Derived Ordered Carbons Predicted by Neural Network Potential.

Shuqi Xie, Kun Ni, Yanwu Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Precision chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Shuqi XieDepartment of Materials Science and Engineering, School of Chemistry and Materials Science, & Hefei National Research Center for Physical Sciences at the Microscale, & State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.
Kun NiDepartment of Materials Science and Engineering, School of Chemistry and Materials Science, & Hefei National Research Center for Physical Sciences at the Microscale, & State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.
Yanwu ZhuDepartment of Materials Science and Engineering, School of Chemistry and Materials Science, & Hefei National Research Center for Physical Sciences at the Microscale, & State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.ORCID https://orcid.org/0000-0002-7505-1502

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Searching for novel carbon allotropes with excellent mechanical and interesting electronic properties is valuable, but such a large structural search remains a challenge if purely based on the traditional density functional theory (DFT) combined with Monte-Carlo (MC) methods. Herein, the neural network potential is utilized to accelerate the sampling of the stochastic surface walking algorithm for a global structural search of ordered carbons from carbon nanotubes (CNTs) under pressure. A variety of unreported ordered carbons are obtained, among which CNTs with diameters smaller than 0.7 nm are more sensitive to pressure than bigger tubes. Most ordered carbons obtained show great thermodynamical and kinetic stability, verified by

Indexed as

CNTs evolutionDFTneural network potentialnovel carbon structuressurface walking algorithm

Identifiers

PMID41170155
PMCPMC12569948

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.