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Wang S, Yam C, Chen S, Hu L, Li L, Hung FF, Fan J, Che CM, Chen G. Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach. J Comput Chem 2024; 45:321-330. [PMID: 37861354 DOI: 10.1002/jcc.27238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/18/2023] [Accepted: 09/23/2023] [Indexed: 10/21/2023]
Abstract
Cyclometalated Pt(II) complexes are popular phosphorescent emitters with color-tunable emissions. To render their practical applications as organic light-emitting diodes emitters, it is required to develop Pt(II) complexes with high radiative decay rate constant and photoluminescence (PL) quantum yield. Here, a general protocol is developed for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield based on the combination of first-principles quantum mechanical method, machine learning, and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than 200 samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen and ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients are 0.96, 0.81, and 0.67 for the predictions of emission wavelength, PL quantum yield and radiative decay rate constant, respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.
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Affiliation(s)
- Shuai Wang
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
| | - ChiYung Yam
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Shuguang Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
| | - LiHong Hu
- School of Information Science and Technology, Northeast Normal University, Changchun, China
| | - Liping Li
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
| | - Faan-Fung Hung
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
- State Key Laboratory of Synthetic Chemistry, HKU-CAS Joint Laboratory on New Materials, The University of Hong Kong, Hong Kong, China
| | - Jiaqi Fan
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
| | - Chi-Ming Che
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
- State Key Laboratory of Synthetic Chemistry, HKU-CAS Joint Laboratory on New Materials, The University of Hong Kong, Hong Kong, China
| | - GuanHua Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong, China
- Hong Kong Quantum AI Lab Limited, Hong Kong, China
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