Predicting the glass transition temperature of polymer based on generative adversarial networks and...
Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning
About this item
Full title
Author / Creator
Liu, Zhanjie , Huo, Yixuan , Chen, Qionghai , Zhan, Siqi , Li, Qian , Zhao, Qingsong , Cui, Lihong and Liu, Jun
Publisher
Wiley-VCH
Journal title
Language
English
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Publisher
Wiley-VCH
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Scope and Contents
Contents
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. A dataset of 68 sets of data from published literature was com...
Alternative Titles
Full title
Predicting the glass transition temperature of polymer based on generative adversarial networks and automated machine learning
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Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_806880635127498492547ad79f41f704
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_806880635127498492547ad79f41f704
Other Identifiers
ISSN
2940-9489
E-ISSN
2940-9497
DOI
10.1002/mgea.78
