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Issue 57, 2018
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Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm

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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.

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

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Publication details

The article was received on 10 Apr 2018, accepted on 25 Jun 2018 and first published on 25 Jun 2018


Article type: Communication
DOI: 10.1039/C8CC02850H
Citation: Chem. Commun., 2018,54, 7955-7958
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    Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm

    J. Qiu, K. Wang, Z. Lian, X. Yang, W. Huang, A. Qin, Q. Wang, J. Tian, B. Tang and S. Zhang, Chem. Commun., 2018, 54, 7955
    DOI: 10.1039/C8CC02850H

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