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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2024-12-01
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| Series: | Materials Genome Engineering Advances |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgea.78 |
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