Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency

Zhaoming He; Hai Bi; Baoyan Liang; Zhiqiang Li; Heming Zhang; Yue Wang

doi:10.1038/s41377-024-01713-w

Go to Nature Website

Your Location：

Home >

Browse articles >

Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency

Original Articles

- Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
- Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
- LSA 2025年14卷第3期页码：800-810
- Affiliations：
  
  1.Jihua Laboratory, Foshan, Guangdong Province, PR China
  2.State Key Laboratory of Supramolecular Structure and Materials, College of Chemistry, Jilin University, Changchun, PR China
  3.Jihua Hengye Electronic Materials CO. LTD, Foshan, Guangdong Province, PR China
- Author bio：
  
  Hai Bi (bihai@jihualab.com)
  Yue Wang (yuewang@jlu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01713-w
  中图分类号：
- 收稿日期：2024-06-16，
  
  修回日期：2024-11-06，
  
  录用日期：2024-12-03，
  
  网络出版日期：2025-02-10，
  
  纸质出版日期：2025-03-31
- Accepted：
Scan QR Code
Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency[J]. LSA, 2025,14(3):800-810.

He, Z. M. et al. Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency. Light: Science & Applications, 14, 800-810 (2025).
Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency[J]. LSA, 2025,14(3):800-810. DOI： 10.1038/s41377-024-01713-w.

He, Z. M. et al. Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency. Light: Science & Applications, 14, 800-810 (2025). DOI： 10.1038/s41377-024-01713-w.

摘要

Abstract

Free of noble-metal and high in unit internal quantum efficiency of electroluminescence

organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation. However

data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed. Inspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures

we developed the Electronic Structure-Infused Network (ESIN) for TADF emitter screening. Designed with capacities of accurate prediction of the photoluminescence quantum yields (PLQYs) of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals (FMOs) weight-based representation and modeling features

ESIN is a promising interpretable tool for emission efficiency evaluation and molecular design of TADF emitters.

关键词

Keywords

references

Uoyama, H. et al. Highly efficient organic light-emitting diodes from delayed fluorescence. Nature 492 , 234–238 (2012)..

Hirata, S. et al. Highly efficient blue electroluminescence based on thermally activated delayed fluorescence. Nat. Mater. 14 , 330–336 (2015)..

Kondo, Y. et al. Narrowband deep-blue organic light-emitting diode featuring an organoboron-based emitter. Nat. Photonics 13 , 678–682 (2019)..

Baldo, M. A. et al. Highly efficient phosphorescent emission from organic electroluminescent devices. Nature 395 , 151–154 (1998)..

Baldo, M. A. et al. Very high-efficiency green organic light-emitting devices based on electrophosphorescence. Appl. Phys. Lett. 75 , 4–6 (1999)..

Jeong, M. et al. Deep learni ng for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs. npj Computational Mater. 8 , 147 (2022)..

Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15 , 1120–1127 (2016)..

Kwon, S.&Yoon, S. DeepCCI: end-to-end deep learning for chemical-chemical interaction prediction. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. Boston, MA, USA: ACM, 2017, 203–212

Joung, J. F. et al. Deep learning optical spectroscopy based on experimental database: potential applications to molecular design. JACS Au 1 , 427–438 (2021)..

Fang, X. M. et al. Geometry-enhanced molecular representation learning for property prediction. Nat. Mach. Intell. 4 , 127–134 (2022)..

Peng, S. P. & Zhao, Y. Convolutional neural networks for the design and analysis of non-fullerene acceptors. J. Chem. Inf. Modeling 59 , 4993–5001 (2019)..

Barcza, S. et al. Structured biological data in the molecular access system. J. Chem. Inf. Computer Sci. 25 , 55–59 (1985)..

Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Modeling 50 , 742–754 (2010)..

Lecun, Y. et al. Gradient-based learning applied to document recognition. Proc. IEEE 86 , 2278–2324 (1998)..

Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60 , 84–90 (2017)..

Lipton, Z. C., Berkowitz, J. & Elkan, C. A critical review of recurrent neural networks forsequence learning. Print at https://doi.org/10.48550/arXiv.1506.00019 https://doi.org/10.48550/arXiv.1506.00019 (2015).

Sperduti, A. & Starita, A. Supervised Neural Networks for the Classification of Structures. IEEE Trans. Neural Netw. Learn. Syst. 8 , 714 (1997)..

Wu, Z. H. et al. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32 , 4–24 (2021)..

Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. Print at https://doi.org/10.48550/arXiv.1206.5538 https://doi.org/10.48550/arXiv.1206.5538 (2012).

Vaswani, A. etal. Attention is all you need. Print at https://doi.org/10.48550/arXiv.1706.03762 https://doi.org/10.48550/arXiv.1706.03762 (2017).

Devlin, J. et al. BERT: pre-training of deep bidirectional transformers for language understanding. Print at https://doi.org/10.48550/arXiv.1810.04805 https://doi.org/10.48550/arXiv.1810.04805 (2018).

Stewart, J. J. P. Optimization of parameters for semiempirical methods Ⅵ: more modifications to the NDDO approximations and re-optimization of parameters. J. Mol. Modeling 19 , 1–32 (2013)..

Stewart, J. J. P. Optimization of parameters for semiempirical methods Ⅰ. Method. J. Computational Chem. 10 , 209–220 (1989)..

Hamilton, W. L., Ying, R. & Leskovec, J. Inductive representation learning on large graphs. Pri nt at https://doi.org/10.48550/arXiv.1706.02216 https://doi.org/10.48550/arXiv.1706.02216 (2017).

Santoro, F. et al. Effective method for the computation of optical spectra of large molecules at finite temperature including the duschinsky and herzberg–teller effect: the Qx band of porphyrin as a case study. J. Chem. Phys. 128 , 224311 (2008)..

Niu, Y. L. et al. Theory of excited state decays and optical spectra: application to polyatomic molecules. J. Phys. Chem. A 114 , 7817–7831 (2010)..

Blum, L. C. & Reymond, J. L. 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc. 131 , 8732–8733 (2009)..

Ramakrishnan, R. et al. Electronic spectra from TDDFT and machine learning in chemical space. J. Chem. Phys. 143 , 084111 (2015)..

Ruddigkeit, L.et al. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Modeling 52 , 2864–2875 (2012)..

Hatakeyama, T. et al. Ultrapure blue ther mally activated delayed fluorescence molecules: efficient HOMO-LUMO separation by the multiple resonance effect. Adv. Mater. 28 , 2777–2781 (2016)..

Zhang, Q. S. et al. Efficient blue organic light-emitting diodes employing thermally activated delayed fluorescence. Nat. Photonics 8 , 326–332 (2014)..

Xu, Y. C. et al. Constructing charge-transfer excited states based on frontier molecular orbital engineering: narrowband green electroluminescence with high color purity and efficiency. Angew. Chem. Int. Ed. 59 , 17442–17446 (2020)..

Xu, Y. C. et al. Highly efficient electroluminescent materials with high color purity based on strong acceptor attachment onto B–N-containing multiple resonance frameworks. CCS Chem. 4 , 2065–2079 (2022)..

Wang, Q. Y. et al. Precise functionalization of a multiple-resonance framework: constructing narrowband organic electroluminescent materials with external quantum efficiency over 40%. Adv. Mater. 35 , e2205166 (2023)..

Chen, T. Q.&Guestrin, C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA: ACM, 2016, 785–794.

Xu, K. Y. L. et al. How powerful are graph neural networks? Print at https://doi.org/10.48550/arXiv.1810.00826 https://doi.org/10.48550/arXiv.1810.00826 (2018).

Xiong, Z. P. et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J. Medicinal Chem. 63 , 8749–8760 (2020)..

Schütt, K. T. et al. SchNet-A deep learning architecture for molecules and materials. J. Chem. Phys. 148 , 241722 (2018)..

Maziarka, Ł. et al. Molecule attention transformer. Print at https://doi.org/10.48550/arXiv.2002.08264 https://doi.org/10.48550/arXiv.2002.08264 (2020).

Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Print at https://doi.org/10.48550/arXiv.1603.04467 https://doi.org/10.48550/arXiv.1603.04467 (2016).

Chen, J. F., Zhu, J. & Song, L. Stochastic training of graph convolutional networks with variance reduction. Print at https://doi.org/10.48550/arXiv.1710.10568 https://doi.org/10.48550/arXiv.1710.10568 (2017).

Krogh, A.&Hertz, J. A. A simple weight decay can improve generalization. Proceedings of the 4th International Conference on Neural Information Processing Systems. Denver, CO, USA: ACM, 1991, 950–957.

Landrum, G. RDKit: Open-source cheminformatics software at https://www.rdkit.org https://www.rdkit.org (2016).

Pracht, P., Bohle, F. & Grimme, S. Automated exploration of the low-energy chemical space with fast quantum chemical methods. Phys. Chem. Chem. Phys. 22 , 7169–7192 (2020)..

Bannwarth, C. et al. Extended tight-binding quantum chemistry methods. WIREs Computational Mol. Sci. 11 , e1493 (2021)..

Bannwarth, C., Ehlert, S. & Grimme, S. GFN2-xTB—An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15 , 1652–1671 (2019)..

Grimme, S., Bannwarth, C. & Shushkov, P. A robust and accurate tight-binding quantum chemical method for st ructures, vibrational frequencies, and noncovalent interactions of large molecular systems parametrized for all spd-block elements ( Z = 1–86). J. Chem. Theory Comput. 13 , 1989–2009 (2017)..

Frisch, M. J. et al. Gaussian 09 Revision D. 01. https://gaussian.com/g09citation/ https://gaussian.com/g09citation/ (2016).

Lu, T. & Chen, F. W. Quantitative analysis of molecular surface based on improved marching tetrahedra algorithm. J. Mol. Graph. Model. 38 , 314–323 (2012)..

Lu, T. & Chen, F. W. Multiwfn: a multifunctional wavefunction analyzer. J. Comput. Chem. 33 , 580–592 (2012)..

Lu, T. & Chen, F. W. Atomic dipole moment corrected hirshfeld population method. J. Theor. Comput. Chem. 11 , 163–183 (2012)..

Lu, T. & Chen, F. W. Comparison of computational methods for atomic charges. Acta Phys.-Chim. Sin. 28 , 1–18 (2012)..

Lu, T. & Chen, Q. X. Independent gradient model based on Hirshfeld partition: a new method for visual study of interactions in chemical systems. J. Comput. Chem. 43 , 539–555 (2022)..

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据