Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementatio...

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Detalles Bibliográficos
Autores principales: Yang, Jason, Tao, Lei, He, Jinlong, McCutcheon, Jeffrey R., Li, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Physical and Materials Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299556/
https://www.ncbi.nlm.nih.gov/pubmed/35857839
http://dx.doi.org/10.1126/sciadv.abn9545

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299556/
https://www.ncbi.nlm.nih.gov/pubmed/35857839
http://dx.doi.org/10.1126/sciadv.abn9545

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