Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data a...

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Detalles Bibliográficos
Autores principales: Chi, Mingzhe, Gargouri, Rihab, Schrader, Tim, Damak, Kamel, Maâlej, Ramzi, Sierka, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747575/
https://www.ncbi.nlm.nih.gov/pubmed/35012054
http://dx.doi.org/10.3390/polym14010026

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8747575/
https://www.ncbi.nlm.nih.gov/pubmed/35012054
http://dx.doi.org/10.3390/polym14010026

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