A statistical learning protocol to resolve the morphological complexity of two-dimensional macromolecules

Unraveling the morphological complexity of two-dimensional macromolecules allows researchers to design and fabricate high-performance, multifunctional materials. Here, we present a protocol based on statistical learning to resolve morphological complexity utilizing geometrical, topological, and phys...

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Detalles Bibliográficos
Autores principales: Zhao, Yingjie, Xu, Zhiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797609/
https://www.ncbi.nlm.nih.gov/pubmed/36223266
http://dx.doi.org/10.1016/j.xpro.2022.101767

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9797609/
https://www.ncbi.nlm.nih.gov/pubmed/36223266
http://dx.doi.org/10.1016/j.xpro.2022.101767

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