Machine Learning with Enormous “Synthetic” Data Sets: Predicting Glass Transition Temperature of Polyimides Using Graph Convolutional Neural Networks

[Image: see text] In the present work, we address the problem of utilizing machine learning (ML) methods to predict the thermal properties of polymers by establishing “structure–property” relationships. Having focused on a particular class of heterocyclic polymers, namely polyimides (PIs), we develo...

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Detalles Bibliográficos
Autores principales: Volgin, Igor V., Batyr, Pavel A., Matseevich, Andrey V., Dobrovskiy, Alexey Yu., Andreeva, Maria V., Nazarychev, Victor M., Larin, Sergey V., Goikhman, Mikhail Ya., Vizilter, Yury V., Askadskii, Andrey A., Lyulin, Sergey V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730753/
https://www.ncbi.nlm.nih.gov/pubmed/36506114
http://dx.doi.org/10.1021/acsomega.2c04649

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730753/
https://www.ncbi.nlm.nih.gov/pubmed/36506114
http://dx.doi.org/10.1021/acsomega.2c04649

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