SOGCN: Prediction of key properties of MR-TADF materials using graph convolutional neural networks

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Li, Yingfu;Zhang, Bohua;Ren, Aimin;Wang, Dongdong;Zhang, Jun;...

通讯作者：

Zou, LY;Nie, CM;Zhang, J

作者机构：

[Ren, Aimin; Zou, Luyi; Su, Zhongmin; Li, Yingfu] Jilin Univ, Coll Chem, Inst Theoret Chem, Changchun 130023, Peoples R China.

[Nie, CM; Nie, Changming; Li, Yingfu] Univ South China, Sch Chem & Chem Engn, Hengyang 421001, Peoples R China.

[Wang, Dongdong; Zhang, Bohua] Xi An Jiao Tong Univ, Sch Chem, Xian 710049, Peoples R China.

[Zhang, J; Zhang, Jun] Shenzhen Bay Lab, Inst Syst & Phys Biol, Shenzhen 518055, Peoples R China.

通讯机构：

[Nie, CM ] U

[Zhang, J ] S

[Zou, LY ] J

Jilin Univ, Coll Chem, Inst Theoret Chem, Changchun 130023, Peoples R China.

Univ South China, Sch Chem & Chem Engn, Hengyang 421001, Peoples R China.

语种：

英文

关键词：

Organic light-emitting diode;Multiple resonance thermal activation delayed;fluorescence;Graph convolutional neural network;Density functional theory;Coupled cluster

期刊：

CHEMICAL ENGINEERING JOURNAL

ISSN：

1385-8947

年：

2024

卷：

501

DOI：

10.1016/j.cej.2024.157676

基金类别：

International Science and Technology Cooperation Project of Jilin Provincial Department of Science and Technology [20240402047GH]; Key Scientific Research Planning Project of Jilin Provincial Education Department [JJKH20241249KJ, JJKH20230511KJ]; Jilin Province Major Science and Technology Special Project [YDZJ202203CGZH030]; Jilin Province Key Research and Development Program [20240302122GX, 20240302015GX]; Natural Science Foundation of Jilin Province of China [20240101167JC]; Open Fund of the State Key Laboratory of Molecular Reaction Dynamics in DICP, CAS

机构署名：

本校为通讯机构

院系归属：

化学化工学院

摘要：

The exploration of the structure and properties of the luminescent materials in OLED devices using Multiple Resonance Thermally Activated Delayed Fluorescence (MR-TADF) is constrained by challenges related to long cycles and high experimental costs, making it a key obstacle in the development of new materials. In response to this challenge, we propose an innovative approach by constructing a graph convolutional neural network model named SOGCN to quickly determine whether an unsynthesized material has the potential to become an MR material, and accurately predict its energy gap and half-peak w...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

SOGCN: Prediction of key properties of MR-TADF materials using graph convolutional neural networks

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问