Progress in organic semiconducting materials with high thermal stability for organic light‐emitting devices

Lee, Soo-Yong; Kim, Hwajeong; Kim, Youngkyoo

doi:10.1002/inf2.12123

Cited by 42 publications

(23 citation statements)

References 164 publications

(276 reference statements)

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“…Currently, hundreds of articles on OLED materials are published every year 8 . There are a lot of useful data in the literatures, but there is no existing database that organizes OLED materials data.…”

Section: Website For Tg and Td Predictionsmentioning

confidence: 99%

“…Recently, a large number of researchers have paid attention to the OLED materials with thermal stability. 8 The glass transition temperature (Tg) and decomposition temperature (Td, corresponding to 5% weight loss) are the two most important thermal properties for the OLED materials, and closely associated with the performance of OLED devices. 9 In particular, Tg of the OLED materials is one of the most important factors that influences device stability and lifetime since the OLED devices irreversibly deteriorate when heated above their Tg.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning based Framework for Quick Prediction of Tg and Td of OLED Materials

Zhao¹,

Fu²,

Fu³

et al. 2021

Preprint

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Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning based framework by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, throngh which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R 2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model.In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com.We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.

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Section: Website For Tg and Td Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning based Framework for Quick Prediction of Tg and Td of OLED Materials

Zhao¹,

Fu²,

Fu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[7,8] However, the relatively poor device stability has not yet been fully resolved and still governs large-scale OLEDs' marketing. [9][10][11][12][13] Although many efforts have been devoted to device stability improvement, less researches were made on the reliability of OLEDs under harsh environmental conditions. [14,15] In particular, in terms of OLED application, one of the significant issues unsolved is the external pressure that the device may encounter.…”

Section: Introductionmentioning

confidence: 99%

Toward Improved Device Efficiency and Stability of Organic Light‐Emitting Diodes via External Pressure Treatment

Pan

Zhu

et al. 2021

Physica Status Solidi (a)

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Herein, the effect of applied external pressure on the electroluminescence characteristics of organic light‐emitting diodes (OLEDs) is investigated. It is demonstrated that the proper pressure intensity can improve the carrier mobility of organic transport materials and enhance the photoluminescence (PL) intensity. Indeed, the OLED device efficiency is significantly enhanced by applying the pressure of 0.75 MPa for 10 min, achieving >12% increase in current efficiency. Most interestingly, when 0.75 MPa pressure is applied to the specific organic layer instead of the whole device for 10 min, the green fluorescent OLED shows a 20.1% higher current efficiency than that of the reference device. The authors attribute this pressure effect to the closer contact between the organic functional layers and the decreased molecule distance, thereby facilitating more efficient charge transport in OLED device. Furthermore, highly stable OLEDs with long operation lifetime are achieved by specific pressure condition, indicating positive effects of pressure treatment on device stability.

show abstract

“…Recently, a large number of researchers have paid attention to the OLED materials with high thermal stability. 8 The glass transition temperature (Tg) and decomposition temperature (Td, corresponding to 5% weight loss) are the two most important thermal properties of OLED materials, and exert significant influence on the performance of OLED devices. 9 In particular, the Tg of OLED materials is one of the most important factors that influences device stability and lifetime since OLED devices irreversibly deteriorate when heated above their Tg.…”

Section: Introductionmentioning

confidence: 99%

Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materials

Zhao

et al. 2021

Preprint

View full text Add to dashboard Cite

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R 2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model. In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com.We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.

show abstract

Progress in organic semiconducting materials with high thermal stability for organic light‐emitting devices

Cited by 42 publications

References 164 publications

Machine Learning based Framework for Quick Prediction of Tg and Td of OLED Materials

Machine Learning based Framework for Quick Prediction of Tg and Td of OLED Materials

Toward Improved Device Efficiency and Stability of Organic Light‐Emitting Diodes via External Pressure Treatment

Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materials

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