Predicting room temperature phosphorescence of new organic molecules by combining physical models and data-driven methods
- 1Department of Chemistry, University of Liverpool Liverpool L69 3BX UK
Abstract
The rational discovery of new RTP emitters remains challenging because phosphorescence depends on multiple competing processes, including intersystem crossing, nonradiative decay, and competition with fluorescence. While several mechanistic hypotheses have been proposed, like enhanced spin–orbit coupling, reduced energy gap between singlet and triplet states, and favourable electronic transition properties, the extent to which these factors can quantitatively predict RTP behaviour remains unclear. In this work, we have collected a dataset of 218 metal-free organic RTP emitters from the literature. We first explored physical approaches to predict phosphorescence yield using descriptors derived from established mechanisms and hypotheses, such as favourable spin–orbit coupling, reorganization energy, and oscillator strength. Although these quantities show statistically meaningful correlations with RTP behaviour, the physical models alone are insufficient to construct a robust quantitative predictive tool. We therefore developed a data-driven machine learning (ML) model based on the Random Forest algorithm, combining these physically motivated descriptors with additional topological descriptors derived from the molecular structure. After feature selection, the optimized models achieved prediction accuracies of 0.774 and 0.820 identifying long-lifetime (lifetime > 90 ms) and long-wavelength (RTP emission > 525 nm) RTP materials, respectively. With the boundary from the physical process and ML analysis, 65 candidates are identified from 48 168 molecules with predicted probabilities greater than 0.75 for both long-lifetime and long-wavelength properties, providing a focused set of promising materials for further experimental validation.
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BibTeX
@article{wu2026predicting,
title = {{Predicting room temperature phosphorescence of new organic molecules by combining physical models and data-driven methods}},
author = {Xia Wu and Alessandro Troisi},
journal = {Chemical Science},
year = {2026},
month = sep,
doi = {10.1039/d6sc06125g},
url = {https://doi.org/10.1039/d6sc06125g},
note = {CC BY 3.0},
}
RIS
TY - JOUR
TI - Predicting room temperature phosphorescence of new organic molecules by combining physical models and data-driven methods
AU - Xia Wu
AU - Alessandro Troisi
PY - 2026
DA - 2026/09/08
JO - Chemical Science
DO - 10.1039/d6sc06125g
UR - https://doi.org/10.1039/d6sc06125g
AB - The rational discovery of new RTP emitters remains challenging because phosphorescence depends on multiple competing processes, including intersystem crossing, nonradiative decay, and competition with fluorescence. While several mechanistic hypotheses have been proposed, like enhanced spin–orbit coupling, reduced energy gap between singlet and triplet states, and favourable electronic transition properties, the extent to which these factors can quantitatively predict RTP behaviour remains unclear. In this work, we have collected a dataset of 218 metal-free organic RTP emitters from the literature. We first explored physical approaches to predict phosphorescence yield using descriptors derived from established mechanisms and hypotheses, such as favourable spin–orbit coupling, reorganization energy, and oscillator strength. Although these quantities show statistically meaningful correlations with RTP behaviour, the physical models alone are insufficient to construct a robust quantitative predictive tool. We therefore developed a data-driven machine learning (ML) model based on the Random Forest algorithm, combining these physically motivated descriptors with additional topological descriptors derived from the molecular structure. After feature selection, the optimized models achieved prediction accuracies of 0.774 and 0.820 identifying long-lifetime (lifetime > 90 ms) and long-wavelength (RTP emission > 525 nm) RTP materials, respectively. With the boundary from the physical process and ML analysis, 65 candidates are identified from 48 168 molecules with predicted probabilities greater than 0.75 for both long-lifetime and long-wavelength properties, providing a focused set of promising materials for further experimental validation.
SN - 2041-6520
SN - 2041-6539
ER -