先進的機器學習驅動的高通量方法，用於合理設計多共振熱激活延遲螢光材料

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姓名

黃義生(YI-Sheng Huang) 查詢紙本館藏

畢業系所

化學學系

論文名稱

先進的機器學習驅動的高通量方法，用於合理設計多共振熱激活延遲螢光材料
(Advanced Machine Learning-Driven High-Throughput Approach for the Rational Design of Multi-Resonance Thermally Activated Delayed Fluorescence Materials)

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至系統瀏覽論文 (2026-7-26以後開放)

摘要(中)

熱活化延遲熒光 (TADF) 技術通過提高效率、色純度和成本效益，特別在智慧型手機和液晶顯示器 (LCD) 中，增強了有機發光二極體 (OLEDs)。含有硼的多共振 (MR) TADF 材料具有色純度高和窄的發射光譜。有效的反系統間跨越 (RISC) 過程對實現 100% 的內部量子效率至關重要。然而，設計潛在的 MR-TADF 材料仍具挑戰，因其靈活的分子設計以及準確預測 RISC 速率的困難性。在此，我們提出了一種先進的機器學習 (ML) 驅動的高通量虛擬篩選 (HTVS) 方法，用於合理設計 MR-TADF 材料。通過利用 156 個同行評審的 MR-TADF 材料數據庫，我們開發了基於 XGBoost、ANN 和 CNN 學習器的 ML 預測模型，使用從密度泛函理論 (DFT) 計算得出的分子描述符集合 (MDS)。我們的模型在kRISC的預測精度上達到了很高的水平，其中考慮了激發態特性的 ANN/MDS-B* 模型表現最為優越。我們使用這些模型篩選了基於 11 個 MR-TADF 核心設計的 2,288 個新 MR-TADF 候選物，識別出具有高 kRISC 值的有希望分子，並推導出進一步的化學設計規則。本研究強調了基於 ML 的 HTVS 在加速發現高性能 MR-TADF 材料方面的有效性，闡明了設計實驗性 OLEDs 所需的重要供體 MR-TADF 核心架構。

摘要(英)

Thermally activated delayed fluorescence (TADF) technology enhances OLEDs with efficiency, color purity, and cost-effectiveness, especially in smartphones and LCDs. Multi-Resonance (MR) TADF materials with boron offer high color purity and narrow emission spectra. Efficient reverse intersystem crossing (RISC) processes are crucial for achieving 100% internal quantum efficiency. However, designing potential MR-TADF materials remains challenging due to their flexible molecular design and the difficulty in accurately predicting RISC rates. Here, we present an advanced machine learning (ML) driven high-throughput virtual screening (HTVS) approach for the rational design of MR-TADF materials. By utilizing a database of 156 peer-reviewed MR-TADF materials, we developed ML predictive models based on XGBoost, ANN, and CNN learners, employing molecular descriptor sets (MDS) derived from density functional theory (DFT) calculations. Our models achieved high predictive accuracy for kRISC, with the ANN/MDS-B* model, which considers the excited-state properties, demonstrating superior performance. We utilized these models to screen 2,288 newly designed MR-TADF candidates based on 11 MR-TADF cores, identifying promising molecules with high kRISC, values and deriving further chemical design rules. The study underscores the effectiveness of ML-based HTVS in expediting the discovery of high-performance MR-TADF materials, elucidating important donor MR-TADF core architectures for designing advanced organic light-emitting diodes (OLEDs) for experimental discovery.

關鍵字(中)

★ 機器學習
★ 熱活化延遲螢光
★ 有機發光二極體

關鍵字(英)

★ Machine Learning
★ Thermally Activated Delayed Fluorescence
★ Organic Light Emitting Diodes

論文目次

Contents
摘要 I
Abstract II
Contents III
List of Figures IV
List of Scheme VI
List of Tables VII
Chapter 1-Introduction 1
Chapter 2-Methods 6
2.1 Database Construction 6
2.2 Computational Details 6
2.3 Molecular Descriptors and Molecular Descriptor Sets 7
2.4 ML Model Optimization and Model Performance Evaluation 9
Chapter 3-Results and Discussion 10
3.1 MD Extraction 10
3.2 Performance Evaluation on ML Models for Predicting RISC Rate Constant 12
3.3 Global Explanations for MDS Affecting kRISC of MR-TADF by SHAP Theory 14
3.4 Model Validation by Predicting kRISC of Uncharted MR-TADF Molecules 17
3.5 Virtual Screening of Potential High-Performance MR-TADF Candidates 21
Chapter 4-Conclusion 35
Supporting Information 37
1. Calculations of Significant Molecular Descriptors 77
1-1 Reorganization Energy (λ) 77
1-2 Spin-Orbit Coupling (SOC) 78
2. Machine Learning Algorithms 79
2-1 XGBoost 79
2-2 ANN 80
2-3 CNN 81
References 83

List of Figures
Figure 1. Pearson Correlation matrix of correlation coefficients (r) for each possible combination of two MDs. (a).MDS-A set and (b). MDS-B set. 11
Figure 2. Predicted lg10(kRISC) trained by (a) XGBoost, (b) ANN, and (c) CNN algorithms against experimental lg10(kRISC) for the training and test set in terms of MDS-A* set. The blue dashed lines guide that the error is within ±0.5, while the yellow dashed lines denotes an error range of ±1.0. 13
Figure 3. Predicted lg10(kRISC) trained by (a) XGBoost, (b) ANN, and (c) CNN algorithms against experimental lg10(kRISC) for the training and test set in terms of MDS-B* set. The blue dashed lines guide that the error is within ±0.5, while the yellow dashed lines denotes an error range of ±1.0. 14
Figure 4. MD importance ranking plot based on SHAP values. Panels show results for (a) XGBoost/MDS-A*, (b) ANN/MDS-A*, and (c) CNN/MDS-A* models. Top: bar chart displaying the average absolute SHAP value magnitudes. Bottom: each point represents a sample, with each row corresponding to an MD. MDs are ordered in descending order based on the average absolute SHAP value. Dense areas indicate a high accumulation of samples. Colors represent the magnitude of MD values, with red indicating high feature values and blue indicating low MD values. The horizontal axis displays positive and negative SHAP values. 15
Figure 5. MD importance ranking plot based on SHAP values. Panels show results for (a) XGBoost/MDS-A*, (b) ANN/MDS-A*, and (c) CNN/MDS-A* models. Top: bar chart displaying the average absolute SHAP value magnitudes. Bottom: each point represents a sample, with each row corresponding to an MD. MDs are ordered in descending order based on the average absolute SHAP value. Dense areas indicate a high accumulation of samples. Colors represent the magnitude of MD values, with red indicating high feature values and blue indicating low MD values. The horizontal axis displays positive and negative SHAP values. 16
Figure 6. Joint distributions using kernel density estimation for important descriptors and the lg10(kRISC) by ANN/MDS-B* model. (a). △E-ST, (b). HDI-T1, and (c). SOC. 17
Figure 7. Performance assessment and predicted kRISC of 16 newly published MR-TADF compared to their experimentally measured values using various ML Models, including (a). XGBoost/MDSA*, (b). ANN/MDSA*, (c). CNN//MDSA*, (d). XGBoost/MDSB*, (e). ANN/MDSB*, and (f). CNN//MDSB* models. The inset figure displays the distribution of prediction errors (Δlg10(kRISC)) calculated as the predicted lg10(kRISC) minus the experimental lg10(kRISC). 21
Figure 8. The MR cores and donors used to construct new MR-TADF molecules for virtual screening include (a) 11 MR-TADF cores, (b) 26 donor groups, and (c) an illustrative example of how new MR-TADF molecules are assembled using a BBP core. 23
Figure 9. Z-scores of (a). 11 MR-TADF cores and (b). 26 donors for 97 top candidates. The inset of Figure 9(a) shows the Z-scores of 10 MR-TADF cores, excluding the BN2Se2 core. The inset of Figure 9(b) shows the Z-scores of 25 donors, excluding the D24. 25
Figure 10. Z-scores of (a). Boron/Oxygensystem, (b). Boron/Nitrogensystems, (c). Boron/Heteroatomsystems, and (d). Carbonyl/Nitrogensystems 27
Figure S11. Reorganization Energy calculation method 77
Figure S12. XGBoost model structure 79
Figure S13. (a) ANN model structure; (b) The learning process of nodes. 81
Figure S14. CNN model structure. 82

List of Scheme
Scheme 1. The workflow employed in this study. 5

List of Tables
Table 1. The predicted lg10(kRISC) values for 16 newly reported MR-TADF molecules by 6 ML models developed in this study. 19
Table 2. The top 10 MR-TADF molecules predicted by the ANN/MDS-B* model with lg10(kRISC) values greater than 6.60. Molecular structures are included in the table. 29
Table 3. The top 15 MR-TADF molecules without containing Se atom predicted by the ANN/MDS-B* model with lg10(kRISC) values greater than 5.50. Molecular structures are included in the table. 31
Table S4. Chemical structures and their Rate Constants of Intersystem Crossing (kISC) and Reverse Intersystem Crossing (kRISC) for 156 Organic Compounds 37
Table S5. Symbols and physical meanings of 24 molecular descriptors (MD) assembled in the MDS-A. 72
Table S6. Symbols and physical meanings of 16 molecular descriptors (MD) assembled in the MDS-B. 74
Table S7. The hyperparameter values for the ML models using MDS-A*. 75
Table S8. The hyperparameter values for the ML models using MDS-B*. 76

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指導教授

蔡惠旭(Hui-Hsu Gavin Tsai)

審核日期

2024-7-29

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