REFERENCES
1. Jordan, M. I.; Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 2015, 349, 255-60.
2. Qiao, X.; Liu, Z.; Sun, J.; et al. Resistive switching oxides: mechanism, performance, and device-algorithm co-design for artificial intelligence. Adv. Mater. 2026, e17373.
3. Feng, B.; Wang, B.; Lv, L.; et al. Interpreting X-ray diffraction patterns of metal-organic frameworks via generative artificial intelligence. J. Am. Chem. Soc. 2026, 148, 869-78.
4. Yu, Y.; Xie, Z.; Luo, M.; et al. Optimizing toward discovery: AI-driven exploration of Lewis acid-base catalysts for PET glycolysis. J. Am. Chem. Soc. 2026, 148, 4635-44.
5. Bin Faheem, A.; Han, Z.; Wu, D.; Li, H. AI-driven big data frameworks for electrode-electrolyte interphases in batteries. Adv. Mater. 2026, 38, e21975.
6. Agrawal, A.; Choudhary, A. Perspective: Materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL. Mater. 2016, 4, 053208.
7. Jose, R.; Ramakrishna, S. Materials 4.0: materials big data enabled materials discovery. Appl. Mater. Today. 2018, 10, 127-32.
8. Pilania, G. Machine learning in materials science: from explainable predictions to autonomous design. Comput. Mater. Sci. 2021, 193, 110360.
9. Stefańska, M.; Müntener, T.; Hiller, S. Predictions of steady-state photo-CIDNP enhancement by machine learning. J. Am. Chem. Soc. 2025, 147, 27172-8.
10. Lu, S.; Zhou, Q.; Ouyang, Y.; Guo, Y.; Li, Q.; Wang, J. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat. Commun. 2018, 9, 3405.
11. Ali, A.; Park, H.; Mall, R.; et al. Machine learning accelerated recovery of the cubic structure in mixed-cation perovskite thin films. Chem. Mater. 2020, 32, 2998-3006.
12. Lee, S. Y.; Byeon, S.; Kim, H. S.; Jin, H.; Lee, S. Deep learning-based phase prediction of high-entropy alloys: optimization, generation, and explanation. Mater. Design. 2021, 197, 109260.
13. Li, Y.; Guo, W. Machine-learning model for predicting phase formations of high-entropy alloys. Phys. Rev. Mater. 2019, 3, 095005.
14. Gómez-Bombarelli, R.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 2016, 15, 1120-7.
15. Cheng, Z.; Liu, J.; Jiang, T.; et al. Automatic screen‐out of Ir(III) complex emitters by combined machine learning and computational analysis. Adv. Opt. Mater. 2023, 11, 2301093.
16. Jeong, M.; Joung, J. F.; Hwang, J.; et al. Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs. npj. Comput. Mater. 2022, 8, 834.
17. Choi, I.; Amin, A.; Katware, A.; Kang, S. W.; Lee, J. Machine learning algorithm for artificial intelligence-based precise structural modeling in organic light-emitting diodes. ACS. Photonics. 2024, 11, 2938-45.
18. Xu, Y.; Xu, P.; Hu, D.; Ma, Y. Recent progress in hot exciton materials for organic light-emitting diodes. Chem. Soc. Rev. 2021, 50, 1030-69.
19. Li, W.; Pan, Y.; Yao, L.; et al. A hybridized local and charge‐transfer excited state for highly efficient fluorescent OLEDs: molecular design, spectral character, and full exciton utilization. Adv. Opt. Mater. 2014, 2, 892-901.
20. Yang, L.; Kim, V.; Lian, Y.; Zhao, B.; Di, D. High-efficiency dual-dopant polymer light-emitting diodes with ultrafast inter-fluorophore energy transfer. Joule 2019, 3, 2381-9.
21. Liu, B.; Yu, Z. W.; He, D.; et al. Ambipolar D–A type bifunctional materials with hybridized local and charge-transfer excited state for high performance electroluminescence with EQE of 7.20% and CIEy ~ 0.06. J. Mater. Chem. C. 2017, 5, 5402-10.
22. Li, W.; Pan, Y.; Xiao, R.; et al. Employing ~100% excitons in OLEDs by utilizing a fluorescent molecule with hybridized local and charge‐transfer excited state. Adv. Funct. Mater. 2014, 24, 1609-14.
23. Liu, B.; Yuan, Y.; He, D.; et al. High-performance blue OLEDs based on phenanthroimidazole emitters via substitutions at the C6- and C9-positions for improving exciton utilization. Chemistry 2016, 22, 12130-7.
24. Tang, X.; Bai, Q.; Peng, Q.; et al. Efficient deep blue electroluminescence with an external quantum efficiency of 6.8% and CIEy < 0.08 based on a phenanthroimidazole–sulfone hybrid donor–acceptor molecule. Chem. Mater. 2015, 27, 7050-7.
25. Zhu, Y.; Vela, S.; Meng, H.; Corminboeuf, C.; Fumanal, M. Donor–acceptor–donor “hot exciton” triads for high reverse intersystem crossing in OLEDs. Adv. Opt. Mater. 2022, 10, 2200509.
26. Kasha, M. Characterization of electronic transitions in complex molecules. Discuss. Faraday. Soc. 1950, 9, 14-9.
27. Chen, T.; Zheng, L.; Yuan, J.; et al. Understanding the control of singlet-triplet splitting for organic exciton manipulating: a combined theoretical and experimental approach. Sci. Rep. 2015, 5, 10923.
28. Froitzheim, T.; Grimme, S.; Mewes, J. M. Either accurate singlet-triplet gaps or excited-state structures: testing and understanding the performance of TD-DFT for TADF emitters. J. Chem. Theory. Comput. 2022, 18, 7702-13.
29. Diesing, S.; Zhang, L.; Zysman-Colman, E.; Samuel, I. D. W. A figure of merit for efficiency roll-off in TADF-based organic LEDs. Nature 2024, 627, 747-53.
30. Huang, S.; Zhang, Q.; Shiota, Y.; et al. Computational prediction for singlet- and triplet-transition energies of charge-transfer compounds. J. Chem. Theory. Comput. 2013, 9, 3872-7.
31. Ju, C. W.; Bai, H.; Li, B.; Liu, R. Machine learning enables highly accurate predictions of photophysical properties of organic fluorescent materials: emission wavelengths and quantum yields. J. Chem. Inf. Model. 2021, 61, 1053-65.
32. Xu, S.; Liu, X.; Cai, P.; Li, J.; Wang, X.; Liu, B. Machine-learning-assisted accurate prediction of molecular optical properties upon aggregation. Adv. Sci. 2022, 9, e2101074.
33. Sun, W.; Zheng, Y.; Yang, K.; et al. Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci. Adv. 2019, 5, eaay4275.
34. Mao, Y.; Yao, X.; Yu, Z.; An, Z.; Ma, H. Ground-state orbital descriptors for accelerated development of organic room-temperature phosphorescent materials. Angew. Chem. Int. Ed. Engl. 2024, 63, e202318836.
35. Zhang, X.; Ding, B.; Wang, Y.; et al. Machine learning for screening small molecules as passivation materials for enhanced perovskite solar cells. Adv. Funct. Mater. 2024, 34, 2314529.
36. Woon, K. L.; Chong, Z. X.; Ariffin, A.; Chan, C. S. Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning. J. Mol. Graph. Model. 2021, 105, 107891.
37. Sifain, A. E.; Lystrom, L.; Messerly, R. A.; et al. Predicting phosphorescence energies and inferring wavefunction localization with machine learning. Chem. Sci. 2021, 12, 10207-17.
38. Pang, Y.; Peng, Q. Molecular descriptors of excited-state property for high-throughput screening of organic photofunctional materials. J. Phys. Chem. Lett. 2024, 15, 8804-12.
39. He, Z.; Bi, H.; Liang, B.; Li, Z.; Zhang, H.; Wang, Y. Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency. Light. Sci. Appl. 2025, 14, 75.
40. Deng, J.; Liang, J.; Bai, S. Learning intermolecular electronic coupling with molecular-orbital-based descriptors. J. Phys. Chem. Lett. 2024, 15, 12551-60.
41. Comas-Vilà, G.; Salvador, P. Quantification of the donor-acceptor character of ligands by the effective fragment orbitals. Chemphyschem 2024, 25, e202400582.
42. Zeng, S.; Zhao, Y.; Li, G.; Wang, R.; Wang, X.; Ni, J. Atom table convolutional neural networks for an accurate prediction of compounds properties. npj. Comput. Mater. 2019, 5, 223.
43. Blaskovits, J. T.; Fumanal, M.; Vela, S.; Corminboeuf, C. Designing singlet fission candidates from donor–acceptor copolymers. Chem. Mater. 2020, 32, 6515-24.
44. Shan, X.; Lu, X.; Sahoo, S. R.; et al. Bright long-wavelength multi-state TADF emitters. Angew. Chem. Int. Ed. Engl. 2025, 64, e202507793.
45. Fan, T.; Nie, X.; Feng, S.; et al. Efficient circularly polarized near-infrared hybridized local and charge-transfer emitter D-A-D structures with TPA as donor and a chiral (R/S) alkyl-substituted thiadiazolo[3,4-g]quinoxaline. Chem. Eng. J. 2025, 525, 170368.
46. Wang, R.; Hu, T.; Liu, Y.; et al. Highly efficient, red delayed fluorescent emitters with exothermic reverse intersystem crossing via hot excited triplet states. J. Phys. Chem. C. 2020, 124, 20816-26.
47. Chen, X.; Yang, Z.; Li, W.; et al. Nondoped red fluorophores with hybridized local and charge-transfer state for high-performance fluorescent white organic light-emitting diodes. ACS. Appl. Mater. Interfaces. 2019, 11, 39026-34.
48. Pun, A. B.; Campos, L. M.; Congreve, D. N. Tunable emission from triplet fusion upconversion in diketopyrrolopyrroles. J. Am. Chem. Soc. 2019, 141, 3777-81.
49. Naimovicius, L.; Wolek, L.; Zhang, S. K.; Kim, J. E.; Tauber, M. J.; Pun, A. B. Activating solid-state triplet-triplet annihilation upconversion via bulky annihilators. J. Am. Chem. Soc. 2026, 148, 3811-9.
50. Feng, H. J.; Zhang, M. Y.; Jiang, L. H.; Huang, L.; Pang, D. W. Triplet-triplet annihilation upconversion: from molecules to materials. Acc. Chem. Res. 2025, 58, 3543-57.
