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
, Kun Wang
, Zhouyang Lian
, Xing Yang
, Wenhui Huang
, Anjun Qin
, Qian Wang
, Jie Tian^*
, Benzhong Tang
, Shuixing Zhang

^*Corresponding author for this work

South China University of Technology
Guangdong Academy of Medical Sciences
Chinese Academy of Sciences
University of Chinese Academy of Sciences
Peking University
Chinese Academy of Medical Sciences
Hong Kong University of Science and Technology

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

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.

Original language	English
Pages (from-to)	7955-7958
Number of pages	4
Journal	Chemical Communications
Volume	54
Issue number	57
DOIs	https://doi.org/10.1039/c8cc02850h
State	Published - 2018
Externally published	Yes

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10.1039/c8cc02850h

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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.",

author = "Jia Qiu and Kun Wang and Zhouyang Lian and Xing Yang and Wenhui Huang and Anjun Qin and Qian Wang and Jie Tian and Benzhong Tang and Shuixing Zhang",

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AU - Qiu, Jia

AU - Wang, Kun

AU - Lian, Zhouyang

AU - Yang, Xing

AU - Huang, Wenhui

AU - Qin, Anjun

AU - Wang, Qian

AU - Tian, Jie

AU - Tang, Benzhong

AU - Zhang, Shuixing

PY - 2018

Y1 - 2018

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AB - 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.

UR - https://www.scopus.com/pages/publications/85049829215

U2 - 10.1039/c8cc02850h

DO - 10.1039/c8cc02850h

M3 - 文章

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AN - SCOPUS:85049829215

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VL - 54

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ER -

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

Abstract

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