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Related Concept Videos

Photoluminescence: Applications01:14

Photoluminescence: Applications

Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...

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High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings
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Machine Learning-Assisted Discovery of Thermally Activated Delayed Fluorescence Emitters.

Khadijah Mohammedsaleh Katubi1, Amir Badshah2, Norah Alomayrah3

  • 1Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Journal of Fluorescence
|June 20, 2026
PubMed
Summary

This study introduces a machine learning (ML) framework to discover new Thermally Activated Delayed Fluorescence (TADF) emitters. The ML model efficiently screened over 50,000 compounds, identifying 50 promising candidates for advanced materials.

Keywords:
Fluorescent compoundsMachine learningMolecular descriptorsThermally activated delayed fluorescence

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Organic Electronics

Background:

  • Thermally Activated Delayed Fluorescence (TADF) materials are crucial for efficient organic light-emitting diodes (OLEDs).
  • Discovering novel TADF emitters with high efficiency and synthetic feasibility is a significant challenge.
  • Accelerating the screening process for new TADF materials is essential for technological advancement.

Purpose of the Study:

  • To develop and validate a machine learning (ML)-assisted framework for the rapid discovery and screening of novel TADF emitters.
  • To identify high-potential TADF candidates from a large chemical database using computational methods.
  • To explore structure-property relationships governing TADF behavior.

Main Methods:

  • A dataset of 366 known TADF compounds was used to train regression models using molecular descriptors calculated with RDKit.
  • The CatBoost algorithm was selected for its superior performance (R² = 0.845) in predicting TADF-likeness.
  • Over 50,000 compounds from the Harvard Organic Photovoltaic Database (HOPV15) were screened using the trained ML model.
  • Descriptor-based filtering, synthetic accessibility analysis, and t-SNE clustering were employed to identify and analyze promising candidates.

Main Results:

  • The CatBoost ML model achieved high accuracy in predicting TADF properties.
  • 50 high-potential TADF candidates were identified from the HOPV15 database.
  • Structural clustering revealed diverse donor-acceptor frameworks associated with TADF behavior.
  • The framework demonstrated efficient screening capabilities for large chemical libraries.

Conclusions:

  • The integration of cheminformatics and ML provides a powerful approach for accelerating the discovery of novel TADF materials.
  • The developed framework enables rapid screening and identification of efficient and synthetically feasible TADF candidates.
  • This ML-assisted strategy significantly advances the search for next-generation organic electronic materials.