Machine learning analysis of a large set of homopolymers to predict glass transition temperatures
Abstract Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study,...
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| Principais autores: | , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Portfolio
2024-10-01
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| coleção: | Communications Chemistry |
| Acesso em linha: | https://doi.org/10.1038/s42004-024-01305-0 |
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