Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning

Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning

Abstract Solution styrene‐butadiene rubber (SSBR) finds wide applications in high performance tire design and various other fields. This study aims to create a quantitative structure–property relationship (QSPR) model linking SSBR's glass transition temperature (Tg) to its structural properties...

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Bibliographic Details
Main Authors: Zhanjie Liu, Yixuan Huo, Qionghai Chen, Siqi Zhan, Qian Li, Qingsong Zhao, Lihong Cui, Jun Liu
Format: Article
Language:English
Published: Wiley-VCH 2024-12-01
Series:Materials Genome Engineering Advances
Subjects:
generative adversarial networks
glass transition temperature
solution styrene‐butadiene rubber
tree‐based pipeline optimization tool
virtual sample generation
Online Access:https://doi.org/10.1002/mgea.78
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https://doi.org/10.1002/mgea.78

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