Evidence map›Paper›PMID 40285597›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Synergistic Machine Learning Guided Discovery of ABa

Yihan Yun, Mengfan Wu, Zhihua Yang, Guangmao Li, Shilie Pan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Synergistic Machine Learning Guided Discovery of ABaAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    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.

Yihan YunResearch Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.
Mengfan WuResearch Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.ORCID https://orcid.org/0009-0005-8173-881X
Zhihua YangResearch Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.
Guangmao LiResearch Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.
Shilie PanResearch Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.ORCID https://orcid.org/0000-0003-4521-4507

Funding

Key Laboratory Opening Foundation of the Xinjiang Uygur Autonomous Region 2022D04013National Natural Science Foundation of China 22305264Natural Science Foundation of Xinjiang Uygur Autonomous Region 2022D01A333Science and Technology Service Network Initiative of CAS KFJ-STSQYZD-130Xinjiang Major Science and Technology Project 2021A01001
6 · The paper itself

Abstract

Discovering novel infrared functional materials (IRFMs) hold tremendous significance for laser industry. Incorporating artificial intelligence into material discovery has been recognized as a pivotal trend driving advancements in materials science. In this work, an IRFM predictor based on machine learning (ML) is developed for the pre-selection of the most promising candidates, in which interpretable analyses reveal the prior domain knowledge of IRFMs. Under the guidance of this IRFM predictor, a series of selenoborates, ABa

Indexed as

chalcogenidesdata‐driven machine learningnonlinear optical materialsselenoborates

Identifiers

PMID40285597
PMCPMC12199391

What OpenQuestion holds

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LicenceCC BY
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.