Evidence map›Paper›PMID 42048018›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks.

Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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. 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

6 authors.

Li FuGuangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China.ORCID https://orcid.org/0009-0005-0462-7411
Longfei LvGuangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China.
Fan ZhangSchool of Physics, Nanjing University, Nanjing, China.
Si ZhouGuangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China.
Weiwei GaoInstitute of Atom Manufacturing, Nanjing University, Suzhou, China.
Jijun ZhaoGuangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials, School of Physics, South China Normal University, Guangzhou, China.ORCID https://orcid.org/0000-0002-3263-7159

Funding

National Natural Science Foundation of China 12474168National Natural Science Foundation of China 12474221National Natural Science Foundation of China 12534012
6 · The paper itself

Abstract

Singlet fission (SF) provides a promising strategy for surpassing the Shockley-Queisser limit in photovoltaics, thereby enabling high-efficiency, sustainable solar energy harvesting. However, the identification of efficient SF materials is hindered by the limited availability of suitable molecular candidates and the high computational costs associated with conventional quantum-chemical methods for excited states. In this study, we introduce a high-throughput screening framework that integrates a graph neural network (GNN) with multi-level validation to accelerate the discovery of promising SF candidates. Trained on a previously reported FORMED database, the GNN yields highly accurate predictions for SF-relevant excited-state properties, demonstrating a mean absolute error of about 0.1 eV for S

Indexed as

excited statesgraph neural networksinglet fissiontime‐dependent density functional theory

Identifiers

PMID42048018
PMCPMC13335501

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.