Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks.

Hu, Junyang; Li, Zean; Lin, Jiaping; Zhang, Liangshun

Hu, Junyang; Li, Zean; Lin, Jiaping; Zhang, Liangshun.

Afiliação

Hu J; Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
Li Z; Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
Lin J; Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
Zhang L; Shanghai Key Laboratory of Advanced Polymeric Materials, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

ACS Appl Mater Interfaces ; 15(46): 54006-54017, 2023 Nov 22.

Article em En | MEDLINE | ID: mdl-37934171

Palavras-chave

glass transition temperature; graph convolutional neural networks; interpretability; machine learning; structure−property relationship

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: ACS Appl Mater Interfaces Assunto da revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo

Adicionar na Minha BVS

Imprimir

XML

PubMed Links

Buscar no Google