Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm

Jia Qiu; Kun Wang; Zhouyang Lian; Xing Yang; Wenhui Huang; Anjun Qin; Qian Wang; Jie Tian; Benzhong Tang; Shuixing Zhang

doi:10.1039/C8CC02850H

Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm†

Jia Qiu,‡^abc Kun Wang,‡^cd Zhouyang Lian,‡^b Xing Yang,^e Wenhui Huang,^ab Anjun Qin,

^f Qian Wang,^g Jie Tian,*^cd Benzhong Tang*^fh and Shuixing Zhang

*^b

Author affiliations

* Corresponding authors

^a School of Medicine, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China

^b Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China
E-mail: shui7515@126.com

^c CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
E-mail: tian@ieee.org

^d University of Chinese Academy of Sciences, Beijing 100049, China

^e Department of Nuclear Medicine, Peking University First Hospital, Beijing 100034, China

^f State Key Laboratory of Luminescent Materials and Devices, South China University of Technology, Guangzhou 510640, China

^g Department of Diagnostic Imaging, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17, Panjiayuan, Chaoyang District, Beijing 100021, China

^h Department of Chemistry, Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and Reconstruction, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, China
E-mail: tangbenz@ust.hk

Abstract

Significant effort has been devoted to the research of aggregation-induced emission (AIE); however, the discovery of new AIE materials is driven mainly by laborious trial-and-error. In this study, taking triphenylamine (TPA)-based luminophores as an example, we propose an efficient machine-learning scheme for predicting AIE-activity based on quantum mechanics.

Chemical Communications

Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm†

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Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm

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