Evidence map›Paper›PMID 41625223›Full record

ArticleACS central science2026

Deep Learning Framework for Atomic-Level Design and Presynthesis Prediction of Coinage-Metal Nanoclusters.

Jiayi Wang, Chunwei Dong, Xiaochuan Gou, Shaopeng Fu, Peng Yuan, Xin Song, Mohammad Bodiuzzaman, Mutalifu Abulikemu, Wanyu Lin, Ren-Wu Huang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS central science, 2026. 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

13 authors.

Jiayi WangCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Chunwei DongInnovation Institute of Carbon Neutrality, Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, People's Republic of China.
Xiaochuan GouCenter of Excellence for Generative AI, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Shaopeng FuCenter of Excellence for Generative AI, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Peng YuanCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0003-1508-6460
Xin SongCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0003-3342-9455
Mohammad BodiuzzamanCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-8316-9359
Mutalifu AbulikemuCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-8848-6804
Wanyu LinDepartment of Data Science and Artificial Intelligence, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
Ren-Wu HuangHenan Key Laboratory of Crystalline Molecular Functional Materials, College of Chemistry, Pingyuan Laboratory, Zhengzhou University, Zhengzhou 450001, P.R. China.
Omar F MohammedCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0001-8500-1130
Di WangCenter of Excellence for Generative AI, Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Osman M BakrCenter of Excellence for Renewable Energy and Storage Technologies, Division of Physical Science and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0002-3428-1002

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The atomically precise nature of coinage-metal nanoclusters (CMNs) enables systematic exploration of structure-property relationships and motivates application oriented inverse design. However, the synthesis of CMNs typically relies on trial-and-error methods, with atomic-level structures only revealed through crystallography (postsynthesis), posing a major challenge to the deterministic synthesis of predesigned cluster structures, which is known as

Identifiers

PMID41625223
PMCPMC12856671

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.