Evidence map›Paper›PMID 39321389›Full record

ArticleAngewandte Chemie (International ed. in English)2025

Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization.

Luca Schaufelberger, J Terence Blaskovits, Ruben Laplaza, Kjell Jorner, Clemence Corminboeuf

Abstract read
In one paragraph

Article in Angewandte Chemie (International ed. in English), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
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  5. Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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

5 authors.

Luca SchaufelbergerÉcole polytechnique fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-, 1015.ORCID https://orcid.org/0000-0002-8239-4351
J Terence BlaskovitsÉcole polytechnique fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-, 1015.ORCID https://orcid.org/0000-0002-1452-5508
Ruben LaplazaÉcole polytechnique fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-, 1015.ORCID https://orcid.org/0000-0001-6315-4398
Kjell JornerETH Zürich, Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 1, Zürich, Switzerland, CH-8093.ORCID https://orcid.org/0000-0002-4191-6790
Clemence CorminboeufÉcole polytechnique fédérale de Lausanne (EPFL), Institute of Chemical Sciences and Engineering, Lausanne, Switzerland, CH-, 1015.ORCID https://orcid.org/0000-0001-7993-2879

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 18054
6 · The paper itself

Abstract

Singlet fission has shown potential for boosting the efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger donor-acceptor systems, these units exhibit localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells.

Indexed as

Genetic AlgorithmInverse DesignMachine LearningSinglet FissionUncertainty Quantification

Identifiers

PMID39321389
PMCPMC11735885

What OpenQuestion holds

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LicenceCC BY-NC
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.