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<p>Organic photodetectors (OPDs) have attracted significant attention owing to their flexible structures, high operational capabilities, and potential for low-cost fabrication, making them promising candidates for next-generation optoelectronic applications. Herein, we investigated a novel OPD configuration comprising a&#xa0;PTB7-Th:PC71BM matrix blended with BPQDs with GO as hole transport layer (HTL)&#xa0;and PDINO as electron transport layer (ETL). The optimized OPD achieved a responsivity of 0.33 A/W coupled with a detectivity of 3.35 × 10<sup>12</sup> Jones. The device demonstrated a short-circuit current density (<i>J</i><sub><i>sc</i></sub>) of 19.35&#xa0;mA/cm<sup>2</sup>, an open-circuit voltage (<i>V</i><sub><i>oc</i></sub>) of 0.89 V, and a fill factor (FF) of 68.06%, resulting in a power conversion efficiency (PCE) of 11.75% under AM 1.5G illumination (100&#xa0;mW&#xa0;cm⁻<sup>2</sup>, 300 K). The incorporation of BPQDs into PTB7-Th:PC71BM resulted in superior charge transport capabilities and reduced recombination, which improved the device performance metrics. Machine learning-assisted modeling revealed that ensemble algorithms significantly enhance the predictive accuracy of the photodetector responsivity. Random Forest Regression achieved the highest performance, with an MSE of 0.0001362, RMSE of 0.0117, and <i>R</i><sup>2</sup> of 0.9108, followed by XGBoost with an <i>R</i><sup>2</sup> of 0.9028. Feature importance analysis identified the active-layer thickness (t-active) and HTL thickness (t-htl) as the most influential parameters. These findings underscore the value of combining simulations with machine learning to optimize organic photodetector design.</p>

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Numerical simulation and machine learning-driven optimization of PTB7-Th: PC71BM organic photodetectors enhanced with BP quantum dots

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Umar Farooq Ali,
  • Ghazi Aman Nowsherwan,
  • Saira Riaz,
  • Shahzad Naseem

摘要

Organic photodetectors (OPDs) have attracted significant attention owing to their flexible structures, high operational capabilities, and potential for low-cost fabrication, making them promising candidates for next-generation optoelectronic applications. Herein, we investigated a novel OPD configuration comprising a PTB7-Th:PC71BM matrix blended with BPQDs with GO as hole transport layer (HTL) and PDINO as electron transport layer (ETL). The optimized OPD achieved a responsivity of 0.33 A/W coupled with a detectivity of 3.35 × 1012 Jones. The device demonstrated a short-circuit current density (Jsc) of 19.35 mA/cm2, an open-circuit voltage (Voc) of 0.89 V, and a fill factor (FF) of 68.06%, resulting in a power conversion efficiency (PCE) of 11.75% under AM 1.5G illumination (100 mW cm⁻2, 300 K). The incorporation of BPQDs into PTB7-Th:PC71BM resulted in superior charge transport capabilities and reduced recombination, which improved the device performance metrics. Machine learning-assisted modeling revealed that ensemble algorithms significantly enhance the predictive accuracy of the photodetector responsivity. Random Forest Regression achieved the highest performance, with an MSE of 0.0001362, RMSE of 0.0117, and R2 of 0.9108, followed by XGBoost with an R2 of 0.9028. Feature importance analysis identified the active-layer thickness (t-active) and HTL thickness (t-htl) as the most influential parameters. These findings underscore the value of combining simulations with machine learning to optimize organic photodetector design.