有機發光二極體，作為終極的顯示技術，已經在市場上取得巨大的成功。然而，開發高效率的有機發光二極體元件，需要良好的元件設計，其中包括合適的主客體組合。在尋找最佳的主客體組合時，通常需要耗時且費力的元件製作過程。因此，非常需要開發一種快速準確的效率預測工具以縮短元件開發流程。
在本研究藉由機器學習，以深度神經網路與隨機森林模型演算法，分別建構出R2=0.89與0.92之能量效率與色座標預測模型。只要輸入主體與客體的最高佔據分子軌域，最低未占據分子軌域，三重態能階與客體的摻雜濃度共七個參數，便可預測元件的能量效度與色座標。模型的表現更通過實驗驗證，在預測藍光、綠光、紅光元件的能量效度時，在100和1,000 nits下平均誤差分別為14%、10%、15%和0%、28%、39%，預測色座標為11%、10%和19%。接著更進一步分析，探討模型的應用，分別為：(1)為一發光體選擇合適的主體、(2)尋找影響元件效率的重要因子、(3)透過模型預測，預測出各個顏色的元件最高效率的能階結構。這一系列的結果更進一步證實了基於機器學習之預測模型的可靠性與實用潛力，並擴及應用至OLED元件設計端與材料設計端，期許能夠縮短有機發光二極體元件的開發流程。

Organic light-emitting diodes (OLEDs), as the ultimate display technology, have achieved huge success in the market. The development of highly-efficient OLED devices generally requires suitable device design, including a suitable host-guest combination. However, experimental determinations of optimal host-guest combination often require a time-consuming and laborious fabrication process. Thus, the development of a quick and accurate prediction model is desirable.
Via machine learning (ML), we have constructed power efficacy (PE) and CIE coordinate prediction models, with R2=0.89 and 0.92, through deep neural network and random forest algorithms. As long as seven parameters are input, including the highest occupied molecular orbital(HOMO), the lowest unoccupied molecular orbital(LUMO), the triplet energy (ET)and the doping concentration, the PE and CIE coordinates of the OLED devices can be predicted. The prediction performance of the model is verified by a series of device fabrications. The average discrepancy for predicted PE100 and PE1000 were 14, 10 and 15% and 0, 28 and 39% for blue, green and red OLEDs compared to experimental results. As to the CIE coordinates, the average discrepancies were 11, 10 and 19%, respectively. Moreover, further analysis was performed to find crucial factors that affect the device efficiency. The energy level structure with the predicted highest efficiency of the devices were also performed through model prediction. The series of results further confirm the reliability and practical potential of the ML-based prediction models, which are expected to shorten the development process of OLED devices.

目錄
摘要 I
Abstract II
致謝 III
表目錄 XI
圖目錄 XII
壹、 緒論 1
貳、文獻回顧 3
2-1、有機發光二極體 3
2-1-1、發光機制與原理 3
2-1-2、發光材料與機制 6
2-1-3、主客體能量轉移機制 9
2-1-4、元件效率與影響因素 12
2-1-5、光色與影響因素 15
2-2、機器學習 17
2-2-1、機器學習之簡介 17
2-2-3、機器學習之常見演算法 19
2-2-4、機器學習之應用 27
2-2-5、機器學習於OLED上的應用 30
參、實驗方法 34
3-1、資料集建立 34
3-1-1、收集資料 34
3-1-2、資料預處理 35
3-1-3、資料集分割 36
3-2、模型建立 36
3-2-1、程式碼撰寫與使用演算法 36
3-2-2、模型評估 41
3-2-3、模型優化 42
3-2-4、模型使用 43
3-3、實驗驗證 43
3-3-1、材料的使用名稱與功能 43
3-3-2、使用材料之化學結構 45
3-3-3、元件與基板電路設計 48
3-3-4、玻璃試片清潔與前處理 49
3-3-5、濕式元件旋轉塗佈製程 50
3-3-6、真空薄膜蒸鍍製程 51
3-3-7、有機層之蒸鍍 52
3-3-8、無機層之蒸鍍 52
3-3-9、元件光電特性之量測 53
肆、結果與討論 55
4-1、資料集描述 55
4-2、基於機器學習之表現預測模型 57
4-2-1、元件能量效率預測模型 57
4-2-3、色座標預測模型 58
4-3、模型預測與實驗比較 59
4-3-1、藍光元件 59
4-3-2、綠光元件 62
4-3-4、紅光元件 65
4-4 模型的應用 68
4-4-1 選擇合適的主體 68
4-4-2 元件效率影響因子之分析 69
4-4-3 預測最佳效率的元件結構 72
伍、結論 76
陸、參考文獻 78
附錄、個人著作目錄 88

陸、參考文獻
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