Evidence map›Paper›PMID 39841852›Full record

ArticleScience advances2025

Advancing efficiency in deep-blue OLEDs: Exploring a machine learning-driven multiresonance TADF molecular design.

Hyung Suk Kim, Hyung Jin Cheon, Sang Hoon Lee, Junho Kim, Seunghyup Yoo, Yun-Hi Kim, Chihaya Adachi

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Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  4. Unveiling key descriptorsChemical science · 2026
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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

7 authors.

Hyung Suk KimCenter for Organic Photonics and Electronics Research (OPERA), Kyushu University, 744 Motooka, Nishi, Fukuoka 819-0395, Japan.ORCID 0000-0002-9155-1144
Hyung Jin CheonDepartment of Chemistry, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0009-0000-4233-2693
Sang Hoon LeeDepartment of Applied Chemistry, Kyushu University, 744 Motooka, Nishi, Fukuoka 819-0395, Japan.ORCID 0009-0000-7703-6173
Junho KimSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.ORCID 0000-0001-6564-7970
Seunghyup YooSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.ORCID 0000-0003-1887-6118
Yun-Hi KimDepartment of Chemistry, Gyeongsang National University, Jinju 52828, Republic of Korea.ORCID 0000-0001-8856-4414
Chihaya AdachiCenter for Organic Photonics and Electronics Research (OPERA), Kyushu University, 744 Motooka, Nishi, Fukuoka 819-0395, Japan.ORCID 0000-0001-6117-9604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The pursuit of boron-based organic compounds with multiresonance (MR)-induced thermally activated delayed fluorescence (TADF) is propelled by their potential as narrowband blue emitters for wide-gamut displays. Although boron-doped polycyclic aromatic hydrocarbons in MR compounds share common structural features, their molecular design traditionally involves iterative approaches with repeated attempts until success. To address this, we implemented machine learning algorithms to establish quantitative structure-property relationship models, predicting key optoelectronic characteristics, such as full width at half maximum (FWHM) and main peak wavelength, for deep-blue MR candidates. Using these methodologies, we crafted ν-DABNA-O-xy and developed deep-blue organic light-emitting diodes featuring a Commission Internationale de l'Eclairage y of 0.07 and an FWHM of 19 nm. The maximum external quantum efficiency reached ca. 27.5% with a binary emission layer, which increased to 41.3% with the hyperfluorescent architecture, effectively mitigating efficiency roll-off. These findings are expected to guide the systematic design of MR-type TADF clusters, unlocking their full potential.

Identifiers

PMID39841852
PMCPMC11753437

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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.