Evidence map›Paper›PMID 40624020›Full record

ArticleNature communications2025

Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors.

R A W Ayyubi, Mei Xian Low, Salar Salimi, Majid Khorsandi, M Mosarof Hossain, Hurriyat Arooj, Shoaib Masood, M Husnain Zeb, Nasir Mahmood, Qiaoliang Bao and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 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. 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

12 authors.

R A W AyyubiDepartment of Physics, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID http://orcid.org/0000-0002-9887-6822
Mei Xian LowSchool of Engineering, RMIT University, Melbourne, Victoria, Australia.
Salar SalimiRadiation Application Department, Shahid Beheshti University, Tehran, Iran.ORCID http://orcid.org/0009-0002-7822-0823
Majid KhorsandiDepartment of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.ORCID http://orcid.org/0000-0003-4055-3219
M Mosarof HossainDepartment of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia.
Hurriyat AroojDepartment of Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
Shoaib MasoodDepartment of Physics, University of Illinois at Chicago, Chicago, Illinois, USA.ORCID http://orcid.org/0000-0001-8929-3732
M Husnain ZebDepartment of Electrical and Computer Engineering, Concordia University, 1455 Boul. de Maisonneuve Ouest, Montréal, QC, Canada.
Nasir MahmoodSchool of Science, RMIT University, Melbourne, VIC 3000, Australia.ORCID http://orcid.org/0000-0002-8340-1058
Qiaoliang BaoInstitute of Energy Materials Science (IEMS), University of Shanghai for Science and Technology, Shanghai, China.
Sumeet WaliaSchool of Engineering, RMIT University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0002-3645-9847
Babar ShabbirDepartment of Nuclear, Plasma and Radiological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA. babar.shabbir@rmit.edu.au.ORCID http://orcid.org/0000-0002-8162-8013

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and a persistent challenge in optoelectronic device engineering. Machine learning models often depend on extensive datasets, which are typically lacking in specialized research domains such as extreme ultraviolet (EUV) radiation detection. Here, we demonstrate a Cross-Spectral Response Prediction framework that leverages existing visible and ultraviolet (UV) photoresponse data to predict more efficient material's performance under EUV radiation. Our predictive model, based on Extremely Randomized Trees, correlates physical descriptors with performance across different spectral regions using a comprehensive dataset of 1927 samples. Through this approach, we identified promising materials such as α-MoO

Identifiers

PMID40624020
PMCPMC12234715

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.