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

^*此作品的通讯作者

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

科研成果: 期刊稿件 › 文章 › 同行评审

33 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	7955-7958
页数	4
期刊	Chemical Communications
卷	54
期	57
DOI	https://doi.org/10.1039/c8cc02850h
出版状态	已出版 - 2018
已对外发布	是

访问文件

10.1039/c8cc02850h

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{0c291316beb54e18b05c4d99f1b07c04,

title = "Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm",

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",

note = "Publisher Copyright: {\textcopyright} 2018 The Royal Society of Chemistry.",

year = "2018",

doi = "10.1039/c8cc02850h",

language = "英语",

volume = "54",

pages = "7955--7958",

journal = "Chemical Communications",

issn = "1359-7345",

publisher = "Royal Society of Chemistry",

number = "57",

}

TY - JOUR

T1 - Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm

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

N2 - 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.

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

C2 - 29956696

AN - SCOPUS:85049829215

SN - 1359-7345

VL - 54

SP - 7955

EP - 7958

JO - Chemical Communications

JF - Chemical Communications

IS - 57

ER -

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

摘要

访问文件

其它文件与链接

学术指纹

引用此