High Glass Transition Temperature Fluorinated Polymers Based on Transfer Learning with Small Experimental Data.
Autor: | Yang JH; Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea., Lee J; Interface Materials and Engineering Laboratory, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea., Kwon H; Interface Materials and Engineering Laboratory, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea., Sohn EH; Interface Materials and Engineering Laboratory, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea., Chang H; Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea., Jang S; Chemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea. |
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Jazyk: | angličtina |
Zdroj: | Macromolecular rapid communications [Macromol Rapid Commun] 2024 Aug; Vol. 45 (15), pp. e2400161. Date of Electronic Publication: 2024 Jun 18. |
DOI: | 10.1002/marc.202400161 |
Abstrakt: | Machine learning can be used to predict the properties of polymers and explore vast chemical spaces. However, the limited number of available experimental datasets hinders the enhancement of the predictive performance of a model. This study proposes a machine learning approach that leverages transfer learning and ensemble modeling to efficiently predict the glass transition temperature (T (© 2024 The Author(s). Macromolecular Rapid Communications published by Wiley‐VCH GmbH.) |
Databáze: | MEDLINE |
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