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<p>The development of efficient and sustainable organic semiconductors is crucial for modern power source technologies, as they have the potential to revolutionize the way we harness and utilize energy. For current study, the emission maxima (λ<sub>E</sub>) of 450 organic semiconductors are collected to analyze by machine learning (ML) related Random Forest and gradient boosting regressors. It identifies HallKier, FPdensityMorgan, and SMR_VSA as key descriptors influencing model performance, enabling accurate prediction of λ<sub>E</sub> in organic semiconductors. The results showed that these models were able to predict the λ<sub>E</sub> of the organic semiconductors with high accuracy. Further analysis using SHapley Additive exPlanations (SHAP) values revealed that chemical similarity plays an important role to determine their experimental λ<sub>E</sub>. Interestingly, the study found that the synthetic accessibility (SA) of the organic semiconductors, which refers to the ease with which they can be synthesized, ranged from 0 to 0.20. The highest SA was found to correspond to λ<sub>E</sub> in the range of 350–370&#xa0;nm, which is typically associated with ultraviolet (UV) to blue light emission. This finding suggests that organic semiconductors with high SA tend to have λ<sub>E</sub> in the UV to blue region, which is important for applications such as OLEDs and OPVs.</p>

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Predicting the Experimental Emission Spectra of Fluorescent Organic Semiconductors by Ensemble Machine Learning Analysis

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Javed Akram,
  • Kanwal Ranian,
  • Sohail Nadeem,
  • Mohammed T Alotaibi

摘要

The development of efficient and sustainable organic semiconductors is crucial for modern power source technologies, as they have the potential to revolutionize the way we harness and utilize energy. For current study, the emission maxima (λE) of 450 organic semiconductors are collected to analyze by machine learning (ML) related Random Forest and gradient boosting regressors. It identifies HallKier, FPdensityMorgan, and SMR_VSA as key descriptors influencing model performance, enabling accurate prediction of λE in organic semiconductors. The results showed that these models were able to predict the λE of the organic semiconductors with high accuracy. Further analysis using SHapley Additive exPlanations (SHAP) values revealed that chemical similarity plays an important role to determine their experimental λE. Interestingly, the study found that the synthetic accessibility (SA) of the organic semiconductors, which refers to the ease with which they can be synthesized, ranged from 0 to 0.20. The highest SA was found to correspond to λE in the range of 350–370 nm, which is typically associated with ultraviolet (UV) to blue light emission. This finding suggests that organic semiconductors with high SA tend to have λE in the UV to blue region, which is important for applications such as OLEDs and OPVs.