中文版 | English
题名

基于聚合基元的机器学习探索有机聚合物半导体的迁移率

其他题名
EXPLORING ORGANIC POLYMER SEMICONDUCTOR MOBILITY VIA POLYMER UNIT-BASED MACHINE LEARNING
姓名
姓名拼音
ZHANG Xinyue
学号
12031191
学位类型
博士
学位专业
0702 物理学
学科门类/专业学位类别
07 理学
导师
张文清
导师单位
材料科学与工程系
论文答辩日期
2024-04-25
论文提交日期
2024-06-22
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

   有机聚合物半导体材料(OSC)具有柔性、延展性、制备成本低廉等优势,因此具有广泛的应用前景。在有机半导体材料的众多理化性能中,载流子迁移率是非常关键的性质。高的载流子迁移率意味着更快的响应速度、更低的能量损耗和更好的器件性能。因此,提升有机聚合物半导体的迁移率是一个非常重要的研究任务。显而易见的是,解析聚合物半导体的迁移率构效关系是研发新型高迁移率有机聚合物半导体材料的关键。机器学习是一种以数据和算法为基础的科学,能够从数据中抽取出隐藏的内在关系,并建立模型预测材料性质,解释材料机理或设计具有特定性质的新材料。本文提出了“聚合基元”这一概念,代指组成聚合物的小分子单元。聚合基元之间的组合对聚合物材料的整体性质有着至关重要的影响,比如供体-受体型聚合物当中,可根据供体和受体单元的电子亲和性,电离能对聚合物材料的载流子类型及前线轨道能级等性质实现精确调控。本论文的研究主题是将机器学习与“聚合基元”相结合,探索有机聚合物半导体的构效关系。本论文的研究内容如下:

   针对聚合物的结构特点,提出了“聚合基元”的概念,并设计程序从聚合物数据集中自动识别聚合基元,建立了有机光电材料的“聚合基元”数据库,可应用于相关材料机器学习“结构-性质”关系研究。根据分子输入线性扩展系统(SMILES)的基本规则和聚合基元的结构特征,开发Python程序Python-based Polymer-Unit-recognition script,从聚合物数据库中高通量识别聚合基元。根据聚合基元的结构特征及组成成分对其进行分类,可以整理并汇总出聚合物数据的“聚合基元图书馆”。

   基于聚合基元,构建了“聚合基元分子指纹(Polymer-Unit FingerPrint, PUFp)”的结构表示方法表示有机聚合物半导体结构,并应用于经典机器学习,研究有机聚合物半导体的迁移率及结构基元的构效关系。使用PUFp作为输入特征值,构建了随机森林,支持向量机,多层神经网络,高斯,和K最近邻五种算法模型分别预测聚合物半导体材料的迁移率。PUFp在随机森林算法下,对P型半导体的分类准确率可达74.2%。实现了有机聚合物半导体迁移率预测,筛选出了影响聚合物半导体迁移率的关键聚合基元、揭示了高迁移率的有机聚合物半导体的聚合基元组合规律及设计策略。

   将聚合基元以聚合基元图(Polymer-Unit GraphPUG)的形式应用于图神经网络,研究有机聚合物的构效关系。以聚合基元为节点,聚合基元之间的连接关系为边,可以构建聚合基元图作为深度学习图神经网络的输入。在有机聚合物半导体材料数据集中,以聚合基元图作为输入可以将图神经网络的训练时间减少为原模型的2%。开发了聚合物基元图神经网络可视化模型,提升了模型的训练效率,揭示了影响聚合物半导体迁移率的关键聚合基元及优化策略,改善了机器学习研究聚合物半导体的可解释性。

关键词
语种
中文
培养类别
独立培养
入学年份
2020
学位授予年份
2024-06
参考文献列表

[1] Kim, M.; Ryu, S. U.; Park, S. A.; Choi, K.; Kim, T.; Chung, D.; Park, T., Donor–Acceptor‐Conjugated Polymer for High‐Performance Organic Field‐Effect Transistors: A Progress Report. Advanced Functional Materials [J]2019, 30, 143002.
[2] Shi, L.; Guo, Y.; Hu, W.; Liu, Y., Design and Effective Synthesis Methods for High-Performance Polymer Semiconductors in Organic Field-Effect Transistors. Materials Chemistry Frontiers [J]2017, 1, 2423-2456.
[3] Mahmood, A.; Irfan, A.; Wang, J.-L., Machine Learning for Organic Photovoltaic Polymers: A Minireview. Chinese Journal of Polymer Science [J]2022, 40, 870-876.
[4] Chen, J. Y.; Zhu, M. L.; Shao, M. C.; Shi, W. K.; Yang, J.; Kuang, J. H.; Wang, C. Y.; Gao, W. Q.; Zhu, C.; Meng, R. F.; Yang, Z.; Shao, Z. H.; Zhao, Z. Y.; Guo, Y. L.; Liu, Y. Q., Molecular Design of Multifunctional Integrated Polymer Semiconductors with Intrinsic Stretchability, High Mobility, and Intense Luminescence. Advanced Materials [J]2023, 2423-2456.
[5] Zheng, Y.; Zhang, S.; Tok, J. B. H.; Bao, Z. N., Molecular Design of Stretchable Polymer Semiconductors: CurrentProgress and Future Directions. Journal of the American Chemical Society [J]2022, 144, 4699-4715.
[6] Paterson, A. F.; Singh, S.; Fallon, K. J.; Hodsden, T.; Han, Y.; Schroeder, B. C.; Bronstein, H.; Heeney, M.; McCulloch, I.; Anthopoulos, T. D., Recent Progress in High-Mobility Organic Transistors: A Reality Check. Advanced Materials [J]2018, 30, e1801079.
[7] Iguchi, K., A Study of Hereditary Cerebellar Ataxia. Folia psychiatrica et neurologica japonica [J]1954, 8, 32-43.
[8] Akamatu, H.; Inokuchi, H.; Matsunaga, Y., Organic Semiconductors with High Conductivity .1. Complexes Between Polycyclic Aromatic Hydrocarbons And Halogens. Bulletin of the Chemical Society of Japan [J]1956, 29, 213-218.
[9] Quinn, J. T. E.; Zhu, J.; Li, X.; Wang, J.; Li, Y., Recent Progress in the Development of N-Type Organic Semiconductors For Organic Field Effect Transistors. Journal of Materials Chemistry C [J]2017, 5, 8654-8681.
[10] Gao, X.; Zhao, Z., High Mobility Organic Semiconductors for Field-Effect Transistors. Science China-Chemistry [J]2015, 58, 947-968.
[11] Matsuhisa, N.; Ieee In Rubber-Like Stretchable Electronics For Skin-Conformable Wearable Devices, 11th IEEE CPMT Symposium Japan (ICSJ), Kyoto Univ, Kyoto, Japan, Nov 09-11; Kyoto Univ, Kyoto, Japan, 2022; p 10.1109/ICSJ55786.2022.10034691.
[12] Jia, Y. H.; Jiang, Q. L.; Sun, H. D.; Liu, P. P.; Hu, D. H.; Pei, Y. Z.; Liu, W. S.; Crispin, X.; Fabiano, S.; Ma, Y. G.; Cao, Y., Wearable Thermoelectric Materials and Devices for Self-Powered Electronic Systems. Advanced Materials [J]2021, 33, 2102990.
[13] Zhao, Z. Y.; Liu, K.; Liu, Y. W.; Guo, Y. L.; Liu, Y. Q., Intrinsically Flexible Displays: Key Materials and Devices. National Science Review [J]2022, 9, 35711242.
[14] Nakajima, Y.; Fujisaki, Y.; Takei, T.; Sato, H.; Nakata, M.; Suzuki, M.; Fukagawa, H.; Motomura, G.; Shimizu, T.; Isogai, Y.; Sugitani, K.; Katoh, T.; Tokito, S.; Yamamoto, T.; Fujikake, H., Low-Temperature Fabrication Of 5-In. QVGA Flexible AMOLED Display Driven by Otfts Using Olefin Polymer as the Gate Insulator. Journal of the Society for Information Display [J]2011, 19, 861-866.
[15] Zhou, Z. Q.; Luo, N.; Shao, X. F.; Zhang, H. L.; Liu, Z. T., Hyperbranched Polymers for Organic Semiconductors. Chempluschem [J]2023, 88, e202300261.
[16] Zhang, C.; Xiong, Y.; Gao, M. Y.; Lan, Z. C.; Wu, J. J.; Ye, L., Electrostatically Sprayed Flexible Encapsulation for High-Performance III-V Solar Cells. Solar Rrl [J]2023, 2300836.
[17] Duan, X. L.; Ding, Y.; Liu, R. Y., Stability Enhancement of Silver Nanowire-Based Flexible Transparent Electrodes for Organic Solar Cells. Materials Today Energy [J]2023, 37, 101409.
[18] Yu, G.; Gao, J.; Hummelen, J. C.; Wudl, F.; Heeger, A. J., Polymer Photovoltaic Cells - Enhanced Efficiencies Via a Network of Internal Donor-Acceptor Heterojunctions. Science [J]1995, 270, 1789-1791.
[19] Usta, H.; Facchetti, A.; Marks, T. J., N-Channel Semiconductor Materials Design For Organic Complementary Circuits. Accounts of Chemical Research [J]2011, 44, 501-510.
[20] Li, G.; Shrotriya, V.; Huang, J. S.; Yao, Y.; Moriarty, T.; Emery, K.; Yang, Y., High-Efficiency Solution Processable Polymer Photovoltaic Cells by Self-Organization of Polymer Blends. Nature Materials [J]2005, 4, 864-868.
[21] Shirota, Y.; Okumoto, K.; Doi, H.; Maeda, M.; Yamate, T. In Development of New Emitting Amorphous Molecular Materials for Organic Light-Emitting Diodes, 8th Conference on Organic Light-Emitting Materials and Devices, Denver, CO, Aug 02-04; Denver, CO, 2004; pp 153-160.
[22] Dobbertin, T.; Schneider, D.; Kammoun, A.; Meyer, J.; Werner, O.; Kröger, M.; Riedl, T.; Becker, E.; Schildknecht, C.; Johannes, H. H.; Kowalsky, W. In Inverted Topside-Emitting Organic Light-Emitting Diodes, Conference on Organic Light-Emitting Materials and Devices VII, San Diego, CA, Aug 04-06; San Diego, CA, 2003; pp 150-161.
[23] Nketia-Yawson, B.; Noh, Y.-Y., Recent Progress on High-Capacitance Polymer Gate Dielectrics for Flexible Low-Voltage Transistors. Advanced Functional Materials [J]2018, 28, 1802201.
[24] Lee, W. H.; Park, Y. D., Organic Semiconductor/Insulator Polymer Blends for High-Performance Organic Transistors. Polymers [J]2014, 6, 1057-1073.
[25] Lee, J., Physical Modeling of Charge Transport in Conjugated Polymer Field-Effect Transistors. Journal of Physics D-Applied Physics [J]2021, 54, 143002.
[26] Wu, F. M.; Liu, Y. X.; Zhang, J.; Duan, S. M.; Ji, D. Y.; Yang, H., Recent Advances in High-Mobility and High-Stretchability Organic Field-Effect Transistors: From Materials, Devices to Applications. Small Methods [J]2021, 5, 2100676.
[27] Liu, K.; Ouyang, B.; Guo, X. J.; Guo, Y. L.; Liu, Y. Q., Advances in Flexible Organic Field-Effect Transistors and Their Applications for Flexible Electronics. Npj Flexible Electronics [J]2022, 6, 1.
[28] Huang, Y. J.; Wang, Z. R.; Chen, Z.; Zhang, Q. C., Organic Cocrystals: Beyond Electrical Conductivities and Field-Effect Transistors (FETs). Angewandte Chemie-International Edition [J]2019, 58, 9696-9711.
[29] Ma, X.; Chen, H. Q.; Zhang, P. W.; Hartel, M. C.; Cao, X. N.; Diltemiz, S. E.; Zhang, Q. L.; Iqbal, J.; de Barros, N. R.; Liu, L. Y.; Liu, H., OFET and OECT, Two Types of Organic Thin-Film Transistor Used in Glucose and DNA Biosensors: A Review. Ieee Sensors Journal [J]2022, 22, 11405-11414.
[30] Hoppner, M.; Kheradmand-Boroujeni, B.; Vahland, J.; Sawatzki, M. F.; Kneppe, D.; Ellinger, F.; Kleemann, H., High-Frequency Operation of Vertical Organic Field-Effect Transistors. Advanced Science [J]2022, 9, 2201660.
[31] Seguchi, N.; Tanaka, R.; Fujita, Y.; Matsuda, M., Organic Field-Effect Transistors Based on a Lithium Phthalocyanine Stable Radical Compound. Bulletin of the Chemical Society of Japan [J]2021, 94, 2474-2476.
[32] Salehi, A.; Fu, X. Y.; Shin, D. H.; So, F., Recent Advances in OLED Optical Design. Advanced Functional Materials [J]2019, 29, 1808803.
[33] Sim, S. M.; Yu, J. H.; Cho, K. H.; Lee, S. H., Self-Aligned Bilayer Inkjet Printing Process for Reducing Shadow Area by Auxiliary Electrodes in OLED Lighting. Organic Electronics [J]2022, 111, 106672.
[34] Puentesl, E. A. C.; Rodriguez, A. G.; Santa, F. M., Measurement Tool for Exposure Techniques in X-ray Ionizing Radiation Equipment. International Journal of Advanced Computer Science and Applications [J]2022, 13, 1002-1009.
[35] Hong, G.; Gan, X. M.; Leonhardt, C.; Zhang, Z.; Seibert, J.; Busch, J. M.; Brase, S., A Brief History of OLEDs-Emitter Development and Industry Milestones. Advanced Materials [J]2021, 33, 2005630.
[36] Chen, Y.; Zhang, D. D.; Zhang, Y. W.; Zeng, X.; Huang, T. Y.; Liu, Z. Y.; Li, G. M.; Duan, L., Approaching Nearly 40% External Quantum Efficiency in Organic Light Emitting Diodes Utilizing a Green Thermally Activated Delayed Fluorescence Emitter with an Extended Linear Donor-Acceptor-Donor Structure. Advanced Materials [J]2021, 33, 2103293.
[37] Chan, C. Y.; Tanaka, M.; Lee, Y. T.; Wong, Y. W.; Nakanotani, H.; Hatakeyama, T.; Adachi, C., Stable Pure-Blue Hyperfluorescence Organic Light-Emitting Diodes with High-Efficiency and Narrow Emission. Nature Photonics [J]2021, 15, 203-207.
[38] Jeong, H.; Kim, H. M.; Kim, J.; Jeong, W.; Jang, J., Highly Robust, Flexible Top-Emission Organic Light-Emitting Diode Exhibiting Stable Performance under Infolding of Curvature Radius of 0.32 mm. Advanced Engineering Materials [J]2021, 23, 2100045.
[39] Irwin, M. D., Interfacial Studies in Bulk-Heterojunction Organic Photovoltaic Devices: Performance Effects and Enhancement Mechanisms of P-Nickel Oxide Anode Interlayers and Hydrochloric Acid-Treated Tin-Doped Indium Oxide Anodes.[B] 2009.
[40] Tsang, S. W., Charge Carrier Transport and Injection Across Organic Heterojunctions.[B] 2009.
[41] Kallmann, H.; Pope, M., Photovoltaic Effect in Organic Crystals. Journal of Chemical Physics [J]1959, 30, 585-586.
[42] Bai, Y.; Xue, L. W.; Wang, H. Q.; Zhang, Z. G., Research Advances on Benzotriazole-Based Organic Photovoltaic Materials. Acta Chimica Sinica [J]2021, 79, 820-852.
[43] Gao, Y.; Deng, Y.; Tian, H.; Zhang, J.; Yan, D.; Geng, Y.; Wang, F., Multifluorination toward High-Mobility Ambipolar and Unipolar n-Type Donor-Acceptor Conjugated Polymers Based on Isoindigo. Advanced Materials [J]2017, 29, 1606217.
[44] Paterson, A. F.; Singh, S.; Fallon, K. J.; Hodsden, T.; Han, Y.; Schroeder, B. C.; Bronstein, H.; Heeney, M.; McCulloch, I.; Anthopoulos, T. D., Recent Progress in High-Mobility Organic Transistors: A Reality Check. Advanced Materials [J]2018, 30, e1801079.
[45] Liu, C. Z.; Li, F.; Wang, J. J.; Zhao, X. L.; Zhang, T. M.; Huang, X.; Wu, M. L.; Hu, Z. Y.; Liu, X. M.; Li, Z. T., Self-assembly of Supramolecular Planar Macrocycle Driven by Intermolecular Halogen Bonding. Acta Chimica Sinica [J]2022, 80, 1365-1368.
[46] Yang, Y.; Liu, Z.; Zhang, G.; Zhang, X.; Zhang, D., The Effects of Side Chains on the Charge Mobilities and Functionalities of Semiconducting Conjugated Polymers beyond Solubilities. Advanced Materials [J]2019, 31, e1903104.
[47] Ding, Y.; Zhu, Y.; Wang, X.; Wang, Y.; Zhang, S.; Zhang, G.; Gu, X.; Qiu, L., Side Chain Engineering: Achieving Stretch-Induced Molecular Orientation and Enhanced Mobility in Polymer Semiconductors. Chemistry of Materials [J]2022, 34, 2696-2707.
[48] 胡文平, 有机场效应晶体管.[B] 科学出版社: 2011.
[49] Yin, J.; Chaitanya, K.; Ju, X.-H., Structures And Charge Transport Properties of "Selenosulflower" and Its Selenium Analogue "Selflower": Computer-Aided Design of High-Performance Ambipolar Organic Semiconductors. Journal of Materials Chemistry C [J]2015, 3, 3472-3481.
[50] Ramprasad, R.; Batra, R.; Pilania, G.; Mannodi-Kanakkithodi, A.; Kim, C., Machine Learning in Materials Informatics: Recent Applications and Prospects. npj Computational Materials [J]2017, 3, 177-185.
[51] Topol, E. J., High-Performance Medicine: The Convergence of Human and Artificial Intelligence. Nature Medicine [J]2019, 25, 44-56.
[52] Glover, F., Future Paths for Integer Programming and Links to Artificial-Intelligence. Computers & Operations Research [J]1986, 13, 533-549.
[53] Arrieta, A. B.; Diaz-Rodriguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; Garcia, S.; Gil-Lopez, S.; Molina, D.; Benjamins, R.; Chatila, R.; Herrera, F., Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI. Information Fusion [J]2020, 58, 82-115.
[54] Nahavandi, S., Examining Artificial Intelligence Applications [Editorial]. IEEE Systems, Man, and Cybernetics Magazine [J]2022, 8, 3-3.
[55] LeCun, Y.; Bengio, Y.; Hinton, G., Deep learning. Nature [J]2015, 521, 436-444.
[56] Baskin, II; Winkler, D.; Tetko, I. V., A Renaissance of Neural Networks in Drug Discovery. Expert Opinion on Drug Discovery [J]2016, 11, 785-795.
[57] Zhao, X. G.; Zhou, K.; Xing, B. Y.; Zhao, R. T.; Luo, S. L.; Li, T. S.; Sun, Y. H.; Na, G. R.; Xie, J. H.; Yang, X. Y.; Wang, X. J.; Wang, X. Y.; He, X.; Lv, J.; Fu, Y. H.; Zhang, L. J., JAMIP: An Artificial-Intelligence Aided Data-Driven Infrastructure for Computational Materials Informatics. Science Bulletin [J]2021, 66, 1973-1985.
[58] Gubernatis, J. E.; Lookman, T., Machine Learning in Materials Design and Discovery: Examples From the Present and Suggestions For the Future. Physical Review Materials [J]2018, 2, 120301.
[59] Cheng, M.; Zhang, Z. Y.; Wang, S. H.; Bi, K. X.; Hu, K. Q.; Dai, Z. D.; Dai, Y. Y.; Liu, C.; Zhou, L.; Ji, X.; Shi, W. Q., A Large-Scale Screening of Metal-Organic Frameworks for Iodine Capture Combining Molecular Simulation and Machine Learning. Frontiers of Environmental Science & Engineering [J]2023, 17, 148.
[60] Liu, F. S. J. W. W., Review of Machine Learning Algorithm Applied In Materials Science. New chemical materials [J]2022, 50, 9.
[61] Wong, F.; Zheng, E. J.; Valeri, J. A., Discovery of A Structural Class of Antibiotics with Explainable Deep Learning. Nature [J]2023, 177-185.
[62] Ramprasad, R.; Batra, R.; Pilania, G.; Mannodi-Kanakkithodi, A.; Kim, C., Machine Learning in Materials Informatics: Recent Applications And Prospects. Npj Computational Materials [J]2017, 3, 177-185.
[63] Arifuzzaman, M.; Arslan, E.; Soc, I. C. In Learning Transfers via Transfer Learning, 8th IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS), St Louis, MD, Nov 14-19; St Louis, MD, 2021; pp 34-43.
[64] Taylor, M. E.; Stone, P., Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research [J]2009, 10, 1633-1685.
[65] Krohn-Grimberghe, A.; Busche, A.; Nanopoulos, A.; Schmidt-Thieme, L. In Active Learning for Technology Enhanced Learning, 6th European Conference on Technology-Enhanced Learning (EC-TEL), Palermo, ITALY, Sep 20-23; Palermo, ITALY, 2011; pp 512-518.
[66] Bakker, T.; van Hoof, H.; Welling, M. In Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, ITALY, Sep 18-22; Turin, ITALY, 2023; pp 3-19.
[67] Zhu, T. T.; Liu, Y. Z.; Sun, J. S.; Sun, C. H., Topic Discovery From Short Reviews Based on Data Enhancement. Intelligent Data Analysis [J]2022, 26, 295-310.
[68] Brown, K. A.; Brittman, S.; Maccaferri, N.; Jariwala, D.; Ceano, U., Machine Learning in Nanoscience: Big Data at Small Scales. Nano Letters [J]2020, 20, 2-10.
[69] Kokol, P.; Kokol, M.; Zagoranski, S., Machine Learning on Small Size Samples: A Synthetic Knowledge Synthesis. Science Progress [J]2022, 105, 00368504211029777.
[70] Zhang, T. H.; Guo, X. Q.; Zheng, H.; Liu, Y.; Wulamu, A.; Chen, H.; Guo, X. X.; Zhang, Z. Z., Review on Perovskite-Type Compound Using Machine Learning. Science of Advanced Materials [J]2022, 14, 1001-1017.
[71] Zhang, L.; He, M.; Shao, S. F., Machine Learning for Halide Perovskite Materials. Nano Energy [J]2020, 78, 105380.
[72] Mai, H.; Le, T. C.; Chen, D.; Winkler, D. A.; Caruso, R. A., Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev [J]2022, 122, 13478-13515.
[73] Zhou, X.; Zheng, Z.; Lu, T.; Xu, P.; Chang, T.; Li, M.; Lu, W., Interpretable Machine Learning Assisted Multi-Objective Optimization Design for Small Molecule Hole Transport Materials. Journal of Alloys and Compounds [J]2023, 966, 171440.
[74] Prado, F. F.; Digiampietri, L. A. In A Systematic Review of Automated Feature Engineering Solutions in Machine Learning Problems, 16th Brazilian Symposium on Information Systems - Information Systems on Digital Transformation and Innovation (SBSI), Electr Network, Nov 03-06; Electr Network, 2020; p 12.
[75] Hao, J. G.; Ho, T. K., Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. Journal of Educational and Behavioral Statistics [J]2019, 44, 348-361.
[76] Berthold, M. R.; Cebron, N.; Dill, F.; Di Fatta, G.; Gabriel, T. R.; Georg, F.; Meinl, T.; Ohl, P.; Sieb, C.; Wiswedel, B. In Knime: The konstanz information miner, 4th International Industrial Simulation Conference, Univ Palermo, Palermo, ITALY, Jun 05-07; Univ Palermo, Palermo, ITALY, 2006; pp 58-+.
[77] Landrum, G., Rdkit: Open-Source Cheminformatics From Machine Learning to Chemical Registration. Abstracts of Papers of the American Chemical Society [J]2019, 258.
[78] Kong, Y.; Zhao, X. M.; Liu, R. Z.; Yang, Z. W.; Yin, H. Y.; Zhao, B. W.; Wang, J. L.; Qin, B. J.; Yan, A. X., Integrating Concept of Pharmacophore With Graph Neural Networks for Chemical Property Prediction and Interpretation. Journal of Cheminformatics [J]2022, 14, 52.
[79] Lundberg, S. M.; Erion, G.; Chen, H.; DeGrave, A.; Prutkin, J. M.; Nair, B.; Katz, R.; Himmelfarb, J.; Bansal, N.; Lee, S.-I., From Local Explanations to Global Understanding with Explainable AI for Trees. Nature Machine Intelligence [J]2020, 2, 56-67.
[80] Federico Baldassarre, H. A., Explainability Techniques for Graph Convolutional Networks. Arxiv [J]2019, 1905.13686.
[81] Zhao, K.; Omar, O. H.; Nematiaram, T.; Padula, D.; Troisi, A., Novel Thermally Activated Delayed Fluorescence Materials by High-Throughput Virtual Screening: Going Beyond Donor-Acceptor Design. Journal of Materials Chemistry C [J]2021, 9, 3324-3333.
[82] Wilbraham, L.; Smajli, D.; Heath-Apostolopoulos, I.; Zwijnenburg, M. A., Mapping the Optoelectronic Property Space of Small Aromatic Molecules. Communications Chemistry [J]2020, 3, 14.
[83] Biau, G., Analysis of a Random Forests Model. Journal of Machine Learning Research [J]2012, 13, 1063-1095.
[84] Rebentrost, P.; Mohseni, M.; Lloyd, S., Quantum Support Vector Machine for Big Data Classification. Physical Review Letters [J]2014, 113, 130503.
[85] Rumelhart, D. E.; Hinton, G. E.; Williams, R. J., Learning Representations by Back-Propagating Errors. Nature [J]1986, 323, 533-536.
[86] Guo, H.; Li, Y.; Li, Y.; Liu, X.; Li, J., BPSO-Adaboost-KNN Ensemble Learning Algorithm for Multi-Class Imbalanced Data Classification. Engineering Applications of Artificial Intelligence [J]2016, 49, 176-193.
[87] Alpay, B. A.; Gosink, M.; Aguiar, D., Evaluating Molecular Fingerprint-Based Models of Drug Side Effects Against a Statistical Control. Drug Discovery Today [J]2022, 27, 103364.
[88] Bae, S.-Y.; Lee, J.; Jeong, J.; Lim, C.; Choi, J., Effective Data-Balancing Methods for Class-Imbalanced Genotoxicity Datasets Using Machine Learning Algorithms and Molecular Fingerprints. Computational Toxicology [J]2021, 20, 100178.
[89] Nagasawa, S.; Al-Naamani, E.; Saeki, A., Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest. Journal of Physical Chemistry Letters [J]2018, 9, 2639-2646.
[90] Sun, W., Machine Learning–Assisted Molecular Design and Efficiency Prediction for High-Performance Organic Photovoltaic Materials. Science Advances [J]2019, 5, eaay4275.
[91] Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M.; Palmer, A.; Settels, V.; Jaakkola, T.; Jensen, K.; Barzilay, R., Analyzing Learned Molecular Representations for Property Prediction. Journal of Chemical Information and Modeling [J]2019, 59, 3370-3388.
[92] Lim, J.; Ryu, S.; Park, K.; Choe, Y. J.; Ham, J.; Kim, W. Y., Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. Journal of Chemical Information and Modeling [J]2019, 59, 3981-3988.
[93] Lee, C. K.; Lu, C.; Yu, Y.; Sun, Q.; Hsieh, C. Y.; Zhang, S.; Liu, Q.; Shi, L., Transfer Learning With Graph Neural Networks For Optoelectronic Properties of Conjugated Oligomers. Journal of Chemical Physics [J]2021, 154, 024906.
[94] Malhotra, P.; Biswas, S.; Sharma, G. D., Directed Message Passing Neural Network for Predicting Power Conversion Efficiency in Organic Solar Cells. ACS Appl Mater Interfaces [J]2023, 15, 37741-37747.
[95] Chen, S.; Jung, Y., A Generalized-Template-Based Graph Neural Network for Accurate Organic Reactivity Prediction. Nature Machine Intelligence [J]2022, 4, 772-780.
[96] Lier, B.; Poliak, P.; Marquetand, P.; Westermayr, J.; Oostenbrink, C., BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations. Journal of Physical Chemistry Letters [J]2022, 3812-3818.
[97] Tomonaga, S., On a Relativistically Invariant Formulation of The Quantum Theory of Wave Fields. Progress of Theoretical Physics [J]1946, 1, 27-42.
[98] McDonagh, D.; Skylaris, C.-K.; Day, G. M., Machine-Learned Fragment-Based Energies for Crystal Structure Prediction. Journal of Chemical Theory and Computation [J]2019, 15, 2743-2758.
[99] Zhu, S.-c.; Huang, Z.-b.; Hu, Q.; Xu, L., Pressure Tuned Incommensurability and Guest Structure Transition In Compressed Scandium From Machine Learning Atomic Simulation. Physical Chemistry Chemical Physics [J]2022, 24, 7007-7013.
[100] Tabet, A.; Gebhart, T.; Wu, G.; Readman, C.; Smela, M. P.; Rana, V. K.; Baker, C.; Bulstrode, H.; Anikeeva, P.; Rowitch, D. H.; Scherman, O. A., Applying Support-Vector Machine Learning Algorithms Toward Predicting Host-Guest Interactions with Cucurbit 7 Uril. Physical Chemistry Chemical Physics [J]2020, 22, 14976-14982.
[101] Serillon, D.; Bo, C.; Barril, X., Testing Automatic Methods to Predict Free Binding Energy of Host-Guest Complexes in SAMPL7 Challenge. Journal of Computer-Aided Molecular Design [J]2021, 35, 209-222.
[102] Rizzi, V.; Bonati, L.; Ansari, N.; Parrinello, M., The Role Of Water in Host-Guest Interaction. Nature Communications [J]2021, 12, 93.
[103] Lee, M.-H., Identification of Host-Guest Systems in Green TADF-Based Oleds with Energy Level Matching Based on a Machine-Learning Study. Physical Chemistry Chemical Physics [J]2020, 22, 16378-16386.
[104] Korolev, V. V.; Nevolin, Y. M.; Manz, T. A.; Protsenko, P. V., Parametrization of Nonbonded Force Field Terms for Metal-Organic Frameworks Using Machine Learning Approach. Journal of Chemical Information and Modeling [J]2021, 61, 5774-5784.
[105] Bereau, T.; DiStasio, R. A., Jr.; Tkatchenko, A.; von Lilienfeld, O. A., Non-Covalent Interactions Across Organic and Biological Subsets of Chemical Space: Physics-Based Potentials Parametrized from Machine Learning. Journal of Chemical Physics [J]2018, 148, 241706.
[106] Joung, J. F.; Han, M.; Jeong, M.; Park, S., Beyond Woodward-Fieser Rules: Design Principles of Property-Oriented Chromophores Based on Explainable Deep Learning Optical Spectroscopy. Journal of Chemical Information and Modeling [J]2022, 62, 2933-2942.
[107] Yuan, Q.; Szczypinski, F. T.; Jelfs, K. E., Explainable Graph Neural Networks For Organic Cages. Digital Discovery [J]2022, 1, 127-138.
[108] Jeong, N.; Epsztein, R.; Wang, R.; Park, S.; Lin, S.; Tong, T., Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence. Environ Sci Technol [J]2023, 57, 17851-17862.
[109] Dubey, D. K.; Thakur, D.; Yadav, R. A. K.; Nagar, M. R.; Liang, T.-W.; Ghosh, S.; Jou, J.-H., High-Throughput Virtual Screening of Host Materials and Rational Device Engineering for Highly Efficient Solution-Processed Organic Light-Emitting Diodes. Acs Applied Materials & Interfaces [J]2021, 13, 26204-26217.
[110] Halls, M. D.; Giesen, D. J.; Kwak, H. S.; Goldberg, A.; Hughes, T. F.; Cao, Y.; Gavartin, J. L., High-Throughput Quantum Chemistry And Virtual Screening For Organic Semiconductor Solutions. Abstracts of Papers of the American Chemical Society [J]2014, 247, 0065-7727.
[111] Probst, D., An Explainability Framework for Deep Learning on Chemical Reactions Exemplified by Enzyme-Catalysed Reaction Classification. Journal of Cheminformatics [J]2023, 15, 113.
[112] Li, G.; Xie, Z.; Wang, Q.; Chen, X.; Zhang, Y.; Wang, X., Asymmetric Acceptor-Donor-Acceptor Polymers with Fast Charge Carrier Transfer for Solar Hydrogen Production. Chemistry-a European Journal [J]2021, 27, 939-943.
[113] Guo, X.; Puniredd, S. R.; Baumgarten, M.; Pisula, W.; Muellen, K., Benzotrithiophene-Based Donor-Acceptor Copolymers with Distinct Supramolecular Organizations. Journal of the American Chemical Society [J]2012, 134, 8404-8407.
[114] Li, M.; An, C.; Pisula, W.; Mullen, K., Cyclopentadithiophene-Benzothiadiazole Donor-Acceptor Polymers as Prototypical Semiconductors for High-Performance Field-Effect Transistors. Accounts of Chemical Research [J]2018, 51, 1196-1205.
[115] Moore, G. J.; Bardagot, O.; Banerji, N., Deep Transfer Learning: A Fast and Accurate Tool to Predict the Energy Levels of Donor Molecules for Organic Photovoltaics. Advanced Theory and Simulations [J]2022, 5, 2100511.
[116] Mullen, K.; Pisula, W., Donor-Acceptor Polymers. Journal of the American Chemical Society [J]2015, 137, 9503-5.
[117] Kim, M.; Ryu, S. U.; Park, S. A.; Choi, K.; Kim, T.; Chung, D.; Park, T., Donor-Acceptor-Conjugated Polymer for High-Performance Organic Field-Effect Transistors: A Progress Report. Advanced Functional Materials [J]2020, 30, 143002.
[118] Piyakulawat, P.; Keawprajak, A.; Jiramitmongkon, K.; Hanusch, M.; Wlosnewski, J.; Asawapirom, U., Effect of Thiophene Donor Units on The Optical and Photovoltaic Behavior of Fluorene-Based Copolymers. Solar Energy Materials and Solar Cells [J]2011, 95, 2167-2172.
[119] DeLongchamp, D. M.; Kline, R. J.; Lin, E. K.; Fischer, D. A.; Richter, L. J.; Lucas, L. A.; Heeney, M.; McCulloch, I.; Northrup, J. E., High Carrier Mobility Polythiophene Thin Films: Structure Determination by Experiment and Theory. Advanced Materials [J]2007, 19, 833-+.
[120] Fei, Z.; Pattanasattayavong, P.; Han, Y.; Schroeder, B. C.; Yan, F.; Kline, R. J.; Anthopoulos, T. D.; Heeney, M., Influence of Side-Chain Regiochemistry on the Transistor Performance of High-Mobility, All-Donor Polymers. Journal of the American Chemical Society [J]2014, 136, 15154-15157.
[121] Yang, Y.; Liu, Z.; Zhang, G.; Zhang, X.; Zhang, D., The Effects of Side Chains on the Charge Mobilities and Functionalities of Semiconducting Conjugated Polymers beyond Solubilities. Advanced Materials [J]2019, 31, e1903104.
[122] Kim, G.; Kang, S.-J.; Dutta, G. K.; Han, Y.-K.; Shin, T. J.; Noh, Y.-Y.; Yang, C., A Thienoisoindigo-Naphthalene Polymer with Ultrahigh Mobility of 14.4 cm2/V.s That Substantially Exceeds Benchmark Values for Amorphous Silicon Semiconductors. Journal of the American Chemical Society [J]2014, 136, 9477-9483.
[123] Meager, I.; Nikolka, M.; Schroeder, B. C.; Nielsen, C. B.; Planells, M.; Bronstein, H.; Rumer, J. W.; James, D. I.; Ashraf, R. S.; Sadhanala, A.; Hayoz, P.; Flores, J.-C.; Sirringhaus, H.; McCulloch, I., Thieno 3,2-b thiophene Flanked Isoindigo Polymers for High Performance Ambipolar OFET Applications. Advanced Functional Materials [J]2014, 24, 7109-7115.
[124] Deng, Y.; Sun, B.; He, Y.; Quinn, J.; Guo, C.; Li, Y., (3E,8E)-3,8-Bis(2-oxoindolin-3-ylidene)naphtho- 1,2-b:5,6-b′ difuran-2,7(3H,8H)-dione (INDF) Based Polymers For Organic Thin-Film Transistors With Highly Balanced Ambipolar Charge Transport Characteristics. Chemical Communications [J]2015, 51, 13515-13518.
[125] Sumpter, B. G.; Noid, D. W., Neural Networks and Graph Theory as Computational Tools for Predicting Polymer Properties. Macromolecular Theory and Simulations [J]1994, 3, 363-378.
[126] Queen, O.; McCarver, G. A.; Thatigotla, S.; Abolins, B. P.; Brown, C. L.; Maroulas, V.; Vogiatzis, K. D., Polymer Graph Neural Networks for Multitask Property Learning. Npj Computational Materials [J]2023, 9, 90.
[127] Fratini, S.; Nikolka, M.; Salleo, A.; Schweicher, G.; Sirringhaus, H., Charge Transport in High-Mobility Conjugated Polymers and Molecular Semiconductors. Nature Materials [J]2020, 19, 491-502.
[128] Li, Y.; Sonar, P.; Murphy, L.; Hong, W., High Mobility Diketopyrrolopyrrole (DPP)-Based Organic Semiconductor Materials for Organic Thin Film Transistors and Photovoltaics. Energy & Environmental Science [J]2013, 6, 1684-1710.
[129] Nielsen, C. B.; Turbiez, M.; McCulloch, I., Recent Advances in The Development of Semiconducting DPP-Containing Polymers For Transistor Applications. Advanced Materials [J]2013, 25, 1859-80.
[130] Aldeghi, M.; Coley, C. W., A Graph Representation of Molecular Ensembles for Polymer Property Prediction. Chemical Science [J]2022, 13, 10486-10498.
[131] Borealis Completes Integration Of Polymer Unit. Chemical & Engineering News [J]1998, 76, 16-16.
[132] Kuhne, R.; Hilscherova, K.; Smutna, M.; LeSsmollmann, F.; Schuurmann, G., In Silico Bioavailability Triggers Applied To Direct And Indirect Thyroid Hormone Disruptors. Chemosphere [J]2024, 348, 140611-140611.
[133] Khondkaryan, L.; Tevosyan, A.; Navasardyan, H.; Khachatrian, H.; Tadevosyan, G.; Apresyan, L.; Chilingaryan, G.; Navoyan, Z.; Stopper, H.; Babayan, N., Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes. Toxics [J]2023, 11, 785.
[134] Bon, M.; Bilsland, A.; Bower, J.; McAulay, K., Fragment-Based Drug Discovery-The Importance of High-Quality Molecule Libraries. Molecular Oncology [J]2022, 16, 3761-3777.
[135] Ji, J.; Tang, Q.; Yao, M.; Yang, H.; Jin, Y.; Zhang, Y.; Xi, J.; Singh, D. J.; Yang, J.; Zhang, W., Functional-Unit-Based Material Design: Ultralow Thermal Conductivity in Thermoelectrics with Linear Triatomic Resonant Bonds. Journal of the American Chemical Society [J]2022, 144, 18552-18561.
[136] Barbara De¸bska, B. G.-S. w., Fuzzy Definition of Molecular Fragments in Chemical Structures. Journal of chemical information and computer sciences [J]2000, 40, 325-329.
[137] BRINT, A. T., Algorithms for the Identification of Three-Dimensional Maximal Common Substructures. Journal of chemical information and computer sciences [J]1987, 27, 152-158.
[138] Lind, P., Construction and Use Of Fragment-Augmented Molecular Hasse Diagrams. Journal of Chemical Information and Modeling [J]2014, 54, 387-95.
[139] Weininger, D., SMILES, A Chemical Language And Information-System .1. Introduction To Methodology And Encoding Rules. Journal of Chemical Information and Computer Sciences [J]1988, 28, 31-36.
[140] Lenssen, M. F. J. E., Fast Graph Representation Learning With Pytorch Geometric. Arxiv [J]1903.02428v3
[141] Ma, H. H.; Bian, Y. T.; Rong, Y.; Huang, W. B.; Xu, T. Y.; Xie, W. Y.; Ye, G. Y.; Huang, J. Z., Cross-Dependent Graph Neural Networks for Molecular Property Prediction. Bioinformatics [J]2022, 38, 2003-2009.
[142] Kim, Y.; Jeong, Y.; Kim, J.; Lee, E. K.; Kim, W. J.; Choi, I. S., MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties. Chemistry-an Asian Journal [J]2022, 17, e202200269.
[143] Cao, N. D., MolGAN: An Implicit Generative Model For Small Molecular Graphs. Arxiv [J]2018, 11973.
[144] Zhang, X.; Wei, G.; Sheng, Y.; Bai, W.; Yang, J.; Zhang, W.; Ye, C., Polymer-Unit Fingerprint (PUFp): An Accessible Expression of Polymer Organic Semiconductors for Machine Learning. ACS Appl Mater Interfaces [J]2023, 15, 21537-21548.
[145] Polton, D. J., Installation And Operational Experiences With Maccs (Molecular Access System). Online Review [J]1982, 6, 235-242.
[146] Schulz, T. H., A Generalized Weisfeiler-Lehman Graph Kernel. arXiv [J]2021, 2101.08104v1.
[147] Zheng, Y.; Liang, X.; Zhang, Q.; Sun, W.; Shi, T.; Du, J.; Sun, K., Efficiency Prediction for Organic Photovoltaic Cells Using Molecular Fingerprints and Machine Learning Regression Models. Materials Review [J]2021, 35, 8207-8212.
[148] Gao, K.; Duc Duy, N.; Sresht, V.; Mathiowetz, A. M.; Tu, M.; Wei, G.-W., Are 2D fingerprints still valuable for drug discovery? Physical Chemistry Chemical Physics [J]2020, 22, 8373-8390.
[149] Landrum, G., RDKit: Open-source cheminformatics from machine learning to chemical registration. Abstracts of Papers of the American Chemical Society [J]2019, 258, 7727.
[150] Sun, W. B.; Zheng, Y. J.; Zhang, Q.; Yang, K.; Chen, H. Y.; Cho, Y.; Fu, J. H.; Odunmbaku, O.; Shah, A. A.; Xiao, Z. Y.; Lu, S. R.; Chen, S. S.; Li, M.; Qin, B.; Yang, C.; Frauenheim, T.; Sun, K., Artificial Intelligence Designer for Highly-Efficient Organic Photovoltaic Materials. Journal of Physical Chemistry Letters [J]2021, 12, 8847-8854.
[151] Zhang, Q.; Zheng, Y. J.; Sun, W.; Ou, Z.; Odunmbaku, O.; Li, M.; Chen, S.; Zhou, Y.; Li, J.; Qin, B.; Sun, K., High-Efficiency Non-Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry. Advanced Science (Weinh) [J]2022, 9, e2104742.
[152] Sun, W.; Li, M.; Li, Y.; Wu, Z.; Sun, Y.; Lu, S.; Xiao, Z.; Zhao, B.; Sun, K., The Use of Deep Learning to Fast Evaluate Organic Photovoltaic Materials. Advanced Theory and Simulations [J]2019, 2, 1800116.
[153] Yang, Z.; Sheng, Y.; Zhu, C.; Ni, J.; Zhu, Z.; Xi, J.; Zhang, W.; Yang, J., Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials. Journal of Materiomics [J]2022, 8, 633-639.
[154] Rosenblatt, F., The Perceptron - A Probabilistic Model for Information-Storage and Organization in The Brain. Psychological Review [J]1958, 65, 386-408.
[155] Kingma, D. P., Adam: A Method For Stochastic Optimization. Arxiv [J]2015, 1412.6980v4.
[156] Wadsworth, A.; Chen, H.; Thorley, K. J.; Cendra, C.; Nikolka, M.; Bristow, H.; Moser, M.; Salleo, A.; Anthopoulos, T. D.; Sirringhaus, H.; McCulloch, I., Modification of Indacenodithiophene-Based Polymers and Its Impact on Charge Carrier Mobility in Organic Thin-Film Transistors. Journal of the American Chemical Society [J]2020, 142, 652-664.
[157] Sui, Y.; Deng, Y.; Du, T.; Shi, Y.; Geng, Y., Design Strategies Of N-Type Conjugated Polymers for Organic Thin-Film Transistors. Materials Chemistry Frontiers [J]2019, 3, 1932-1951.
[158] Ortiz, R. P.; Yan, H.; Facchetti, A.; Marks, T., Azine- and Azole-Functionalized Oligo´ and Polythiophene Semiconductors for Organic Thin-Film Transistors. Materials [J]2010, 3, 1533-1558.
[159] Nielsen, C. B.; McCulloch, I., Recent Advances In Transistor Performance Of Polythiophenes. Progress in Polymer Science [J]2013, 38, 2053-2069.
[160] Li, M.; Wang, J.; Xu, W.; Li, L.; Pisula, W.; Janssen, R. A. J.; Liu, M., Noncovalent Semiconducting Polymer Monolayers for High-Performance Field-Effect Transistors. Progress in Polymer Science [J]2021, 117, 101394.
[161] Lei, T.; Wang, J. Y.; Pei, J., Design, Synthesis, and Structure-Property Relationships Of Isoindigo-Based Conjugated Polymers. Accounts of Chemical Research [J]2014, 47, 1117-26.
[162] Kim, Y.; Lim, E., Development of Polymer Acceptors for Organic Photovoltaic Cells. Polymers [J]2014, 6, 382-407.
[163] Hu, W.; Feng, L.; Gu, P.; Dong, H.; Yao, Y., High-Mobility Polymeric Semiconductors. Chinese Science Bulletin [J]2015, 60, 2169-2187.
[164] He, K.; Kumar, P.; Yuan, Y.; Li, Y., Wide Bandgap Polymer Donors for High Efficiency Non-Fullerene Acceptor Based Organic Solar Cells. Materials Advances [J]2021, 2, 115-145.
[165] Facchetti, A., π-Conjugated Polymers for Organic Electronics and Photovoltaic Cell Applications. Chemistry of Materials [J]2010, 23, 733-758.
[166] Chen, H., Recent Advances in High-Mobility Polymeric Semiconductor Materials. Chinese Journal of Organic Chemistry [J]2016, 36, 460-479.
[167] Brebels, J.; Manca, J. V.; Lutsen, L.; Vanderzande, D.; Maes, W., High Dielectric Constant Conjugated Materials for Organic Photovoltaics. Journal of Materials Chemistry A [J]2017, 5, 24037-24050.
[168] FEAST, W. J., Synthesis and Material and Electronic Properties of Conjugated Polymers. Journal of Materials Science & Technology [J]25, 3796-3805.
[169] Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E., Scikit-learn: Machine Learning in Python. Journal Of Machine Learning Research [J]2011, 12, 2825-2830.
[170] Breiman, L., Random forests. Mach Learn [J]2001, 45, 5-32.
[171] Patrick, E. A.; Fischer, F. P., A Generalized K-Nearest Neighbor Rule. Information and Control [J]1970, 16, 128-&.
[172] Kowalski, B. R.; Bender, C. F., K-Nearest Neighbor Classification Rule (Pattern-Recognition) Applied To Nuclear Magnetic-Resonance Spectral Interpretation. Analytical Chemistry [J]1972, 44, 1405-&.
[173] Seeger, M., Gaussian processes for machine learning. International journal of neural systems [J]2004, 14, 69-106.
[174] Murphy, K. P., Machine Learning: A Probabilistic Perspective. The MIT Press: 2012; pp 492-493.
[175] Lightbody, G.; Irwin, G. W., Multi-layer perceptron based modelling of nonlinear systems. Fuzzy Sets and Systems [J]1996, 79, 93-112.
[176] Breiman, L., Bagging predictors. Mach Learn [J]1996, 24, 123-140.
[177] Shi, Y.-q., Recent Progress of Imide-functionalized N-type Polymer Semiconductors. Acta Polymerica Sinica [J], 873-889.
[178] Lei, T.; Cao, Y.; Fan, Y.; Liu, C.-J.; Yuan, S.-C.; Pei, J., High-Performance Air-Stable Organic Field-Effect Transistors: Isoindigo-Based Conjugated Polymers. Journal of the American Chemical Society [J]2011, 133, 6099-6101.
[179] Turkoglu, G.; Cinar, M. E.; Ozturk, T., Thiophene-Based Organic Semiconductors. Topics in Current Chemistry [J]2017, 375, 84.
[180] Lin, Y.; Fan, H.; Li, Y.; Zhan, X., Thiazole-Based Organic Semiconductors for Organic Electronics. Advanced Materials [J]2012, 24, 3087-3106.
[181] Kang, B.; Kim, H. N.; Sun, C.; Kwon, S.-K.; Cho, K.; Kim, Y.-H., pi-Extended Thiazole-Containing Polymer Semiconductor for Balanced Charge-Carrier Mobilities. Macromolecular Rapid Communications [J]2021, 42, 2000741.
[182] Shi, Q.; Zhang, S.; Zhang, J.; Oswald, V. F.; Amassian, A.; Marder, S. R.; Blakey, S. B., (KOBu)-Bu-t-Initiated Aryl C-H Iodination: A Powerful Tool for the Synthesis of High Electron Affinity Compounds. Journal of the American Chemical Society [J]2016, 138, 3946-3949.
[183] Kurata, H.; Takakuwa, H.; Imai, N.; Matsumoto, K.; Kawase, T.; Oda, M., Synthesis And Properties Of A 1,3-Thiazole Extended P-Terphenoquinone. A New Sp(2) Nitrogen Atom-Containing Terquinone Derivative. Bulletin of the Chemical Society of Japan [J]2007, 80, 1402-1404.
[184] Ie, Y.; Sato, C.; Yamamoto, K.; Nitani, M.; Aso, Y., A Thiazole-fused Antiaromatic Compound Containing an s-Indacene Chromophore with a High Electron Affinity. Chemistry Letters [J]2018, 47, 1534-1537.
[185] Chavez, P.; Bulut, I.; Fall, S.; Ibraikulov, O. A.; Chochos, C. L.; Bartringer, J.; Heiser, T.; Leveque, P.; Leclerc, N., An Electron-Transporting Thiazole-Based Polymer Synthesized Through Direct (Hetero)Arylation Polymerization. Molecules [J]2018, 23, 1270.
[186] Zhou, N.; Guo, X.; Ortiz, R. P.; Li, S.; Zhang, S.; Chang, R. P.; Facchetti, A.; Marks, T. J., Bithiophene Imide And Benzodithiophene Copolymers For Efficient Inverted Polymer Solar Cells. Advanced Materials [J]2012, 24, 2242-8.
[187] Wang, Y.; Guo, H.; Harbuzaru, A.; Uddin, M. A.; Arrechea-Marcos, I.; Ling, S.; Yu, J.; Tang, Y.; Sun, H.; Lopez Navarrete, J. T.; Ortiz, R. P.; Woo, H. Y.; Guo, X., (Semi)ladder-Type Bithiophene Imide-Based All-Acceptor Semiconductors: Synthesis, Structure-Property Correlations, and Unipolar n-Type Transistor Performance. Journal of the American Chemical Society [J]2018, 140, 6095-6108.
[188] Wu, N.; He, Z.-Q.; Xu, M.; Xiao, W.-K., Recent Developments of Azatriphenylene Materials as n-Type Organic Semiconductors. Acta Physico-Chimica Sinica [J]2014, 30, 1001-1016.
[189] Min, Y.; Dou, C.; Tian, H.; Geng, Y.; Liu, J.; Wang, L., n-Type Azaacenes Containing B <- N Units. Angewandte Chemie-International Edition [J]2018, 57, 2000-2004.
[190] Chai, J.; Wang, C.; Jia, L.; Pang, Y.; Graham, M.; Cheng, S. Z. D., Synthesis and Electrochemical Properties of a New Class Of Boron-Containing N-Type Conjugated Polymers. Synthetic Metals [J]2009, 159, 1443-1449.
[191] Zhang, Q.; Huang, J.; Wang, K.; Huang, W., Recent Structural Engineering of Polymer Semiconductors Incorporating Hydrogen Bonds. Advanced Materials [J]2022, 34, e2110639.
[192] Zhang, W.; Shi, K.; Lai, J.; Zhou, Y.; Wei, X.; Che, Q.; Wei, J.; Wang, L.; Yu, G., Record High Electron Mobility Exceeding 16 cm(2) V(-1) s(-1) in Bisisoindigo-Based Polymer Semiconductor with a Fully Locked Conjugated Backbone. Advanced Materials [J]2023, e2300145.
[193] Jiang, Y.; Chen, J.; Sun, Y.; Li, Q.; Cai, Z.; Li, J.; Guo, Y.; Hu, W.; Liu, Y., Fast Deposition of Aligning Edge-On Polymers for High-Mobility Ambipolar Transistors. Advanced Materials [J]2019, 31, e1805761.
[194] Gilmer, J., Neural Message Passing for Quantum Chemistry. Arxiv [J]2017, 1704.01212.
[195] Nicola De Cao, T. K., Molgan: An Implicit Generative Model For Small Molecular Graphs. Arxiv [J]2018, 11973.
[196] Zhang, Z.; Friedrich, K., Artificial Neural Networks Applied to Polymer Composites: A Review. Composites Science and Technology [J]2003, 63, 2029-2044.
[197] Lu, X. X.; Giovanis, D. G.; Yvonnet, J.; Papadopoulos, V.; Detrez, F.; Bai, J. B., A Data-Driven Computational Homogenization Method Based on Neural Networks for the Nonlinear Anisotropic Electrical Response of Graphene/Polymer Nanocomposites. Computational Mechanics [J]2019, 64, 307-321.
[198] Jiang, Z. Y.; Zhang, Z.; Friedrich, K., Prediction on Wear Properties of Polymer Composites With Artificial Neural Networks. Composites Science and Technology [J]2007, 67, 168-176.
[199] Gurnani, R.; Kuenneth, C.; Toland, A.; Ramprasad, R., Polymer Informatics at Scale with Multitask Graph Neural Networks. Chemistry of Materials [J]2023, 1560–1567.
[200] Aldeghi, M.; Coley, C. W., A Graph Representation of Molecular Ensembles for Polymer Property Prediction. Chemical Science [J]2022, 13, 10486-10498.
[201] Krizhevsky, A.; Sutskever, I.; Hinton, G. E., ImageNet Classification with Deep Convolutional Neural Networks. Communications of the Acm [J]2017, 60, 84-90.
[202] DeepMind, G. B., Relational Inductive Biases, Deep Learning, and Graph Networks. Arxiv [J]2018, 1806.01261v3.
[203] Cho, K., On the Properties of Neural Machine Translation: Encoder–DecoderApproaches. Arxiv [J]2014, 1409.1259v2.
[204] Sardon, H.; Irusta, L.; Santamaria, P.; Fernandez-Berridi, M. J., Thermal and Mechanical Behaviour of Self-Curable Waterborne Hybrid Polyurethanes Functionalized With (3-Aminopropyl)Triethoxysilane (APTES). Journal of Polymer Research [J]2012, 19, 9956.
[205] Lu, Y.; Ding, Y.; Wang, J.; Pei, J., Research Progress in Isoindigo-Based Polymer Field-Effect Transistor Materials. Chinese Journal of Organic Chemistry [J]2016, 36, 2272-2283.
[206] Usta, H.; Facchetti, A.; Marks, T. J., n-Channel Semiconductor Materials Design for Organic Complementary Circuits. Accounts of Chemical Research [J]2011, 44, 501-510.
[207] Guo, Z.; Yu, P.; Sun, K.; Lei, S.; Yi, Y.; Li, Z., Role of Halogenhalogen Interactions in the 2D Crystallization of N-Semiconductors at the Liquid-Solid Interface. Physical Chemistry Chemical Physics [J]2017, 19, 31540-31544.
[208] Berger, G.; Frangville, P.; Meyer, F., Halogen Bonding for Molecular Recognition: New Developments in Materials and Biological Sciences. Chem Commun (Camb) [J]2020, 56, 4970-4981.
[209] Osaka, I., Semiconducting polymers based on electron-deficient π-building units. Polymer Journal [J]2014, 47, 18-25.
[210] Khim, D., Uniaxial Alignment of Conjugated Polymer Films for High Performance Organic FieldEffect Transistors. Advanced Materials [J]2018, 1705463.
[211] Oo, A. M.; Fan, P.; Zhang, X.; Yu, J., Efficiency Improvement of Planar Inverted Perovskite Solar Cells by Introducing Poly 9,9-Dioctyfluorene-co-benzothiazole into Polytriarylamine as Mixed Hole-Transport Layer. Energy Technology [J]2020, 8, 1901042.
[212] Dutta, G. K.; Guha, S.; Patil, S., Synthesis of Liquid Crystalline Benzothiazole Based Derivatives: A Study of Their Optical and Electrical Properties. Organic Electronics [J]2010, 11, 1-9.
[213] He, M.; Li, J.; Sorensen, M. L.; Zhang, F.; Hancock, R. R.; Fong, H. H.; Pozdin, V. A.; Smilgies, D.-M.; Malliaras, G. G., Alkylsubstituted Thienothiophene Semiconducting Materials: Structure-Property Relationships. Journal of the American Chemical Society [J]2009, 131, 11930-11938.
[214] Omee, S. S.; Louis, S. Y.; Fu, N. H., Scalable Deeper Graph Neural Networks for High-Performance Materials Property Prediction. Patterns [J]2022, 3, 100491.
[215] Deng, D.; Lei, Z.; Hong, X.; Zhang, R.; Zhou, F., Describe Molecules by a Heterogeneous Graph Neural Network with Transformer-like Attention for Supervised Property Predictions. ACS OMEGA [J]2022, 7, 3713-3721.
[216] Wei, Y.; Zhao, M.-M.; Zhao, M.; Lei, M.; Yu, Q., An AMP-Based Network With Deep Residual Learning for mmWave Beamspace Channel Estimation. Ieee Wireless Communications Letters [J]2019, 8, 1289-1292.
[217] Huang, S.; Qiu, D.; Tran, T. D., Approximate Message Passing With Parameter Estimation for Heavily Quantized Measurements. Ieee Transactions on Signal Processing [J]2022, 70, 2062-2077.

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工学院_材料科学与工程系
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张新越. 基于聚合基元的机器学习探索有机聚合物半导体的迁移率[D]. 深圳. 南方科技大学,2024.
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