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Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.

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Title: Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.
Authors: Katubi, Khadijah Mohammedsaleh1 (AUTHOR), Badshah, Amir2 (AUTHOR) amirbadshah@kust.edu.pk, Alomayrah, Norah3 (AUTHOR), Al-Buriahi, M. S.4 (AUTHOR)
Source: Journal of Fluorescence. Jun2026, Vol. 36 Issue 6, p4197-4211. 15p.
Subjects: Machine learning, Delayed fluorescence, Dimensional reduction algorithms, Cheminformatics, Chemical testing, Chemical properties
Abstract: This study presents a machine learning (ML)-assisted framework for the discovery and screening of novel TADF emitters. A dataset of 366 known compounds was used to train regression models based on molecular descriptors calculated via RDKit. Among several algorithms tested, the CatBoost model demonstrated superior performance with an R² of 0.845 on the test set. The trained model was subsequently employed to predict TADF-likeness scores for over 50,000 compounds from the Harvard Organic Photovoltaic Database (HOPV15). Using descriptor-based filtering and synthetic accessibility analysis, 50 high-potential TADF candidates were identified. Structural clustering using t-SNE analysis revealed diverse donor–acceptor frameworks favorable for TADF behavior. The integration of cheminformatics and ML enables rapid screening of chemical libraries and accelerates the discovery of TADF materials with high efficiency and practical synthetic feasibility. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Fluorescence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 195093949
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.
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  Data: <searchLink fieldCode="AR" term="%22Katubi%2C+Khadijah+Mohammedsaleh%22">Katubi, Khadijah Mohammedsaleh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Badshah%2C+Amir%22">Badshah, Amir</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> amirbadshah@kust.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Alomayrah%2C+Norah%22">Alomayrah, Norah</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Al-Buriahi%2C+M%2E+S%2E%22">Al-Buriahi, M. S.</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Fluorescence%22">Journal of Fluorescence</searchLink>. Jun2026, Vol. 36 Issue 6, p4197-4211. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Delayed+fluorescence%22">Delayed fluorescence</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cheminformatics%22">Cheminformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+testing%22">Chemical testing</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+properties%22">Chemical properties</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This study presents a machine learning (ML)-assisted framework for the discovery and screening of novel TADF emitters. A dataset of 366 known compounds was used to train regression models based on molecular descriptors calculated via RDKit. Among several algorithms tested, the CatBoost model demonstrated superior performance with an R² of 0.845 on the test set. The trained model was subsequently employed to predict TADF-likeness scores for over 50,000 compounds from the Harvard Organic Photovoltaic Database (HOPV15). Using descriptor-based filtering and synthetic accessibility analysis, 50 high-potential TADF candidates were identified. Structural clustering using t-SNE analysis revealed diverse donor–acceptor frameworks favorable for TADF behavior. The integration of cheminformatics and ML enables rapid screening of chemical libraries and accelerates the discovery of TADF materials with high efficiency and practical synthetic feasibility. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Fluorescence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1007/s10895-026-04844-y
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: 4197
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Delayed fluorescence
        Type: general
      – SubjectFull: Dimensional reduction algorithms
        Type: general
      – SubjectFull: Cheminformatics
        Type: general
      – SubjectFull: Chemical testing
        Type: general
      – SubjectFull: Chemical properties
        Type: general
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      – TitleFull: Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.
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            NameFull: Katubi, Khadijah Mohammedsaleh
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            NameFull: Badshah, Amir
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            NameFull: Alomayrah, Norah
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            NameFull: Al-Buriahi, M. S.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 36
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            – TitleFull: Journal of Fluorescence
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