Cargando…
Coarse-grained modelling to predict the packing of porous organic cages
How molecules pack has vital ramifications for their applications as functional molecular materials. Small changes in a molecule's functionality can lead to large, non-intuitive, changes in their global solid-state packing, resulting in difficulty in targeted design. Predicting the crystal stru...
Autores principales: | Wolpert, Emma H., Jelfs, Kim E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683088/ https://www.ncbi.nlm.nih.gov/pubmed/36507173 http://dx.doi.org/10.1039/d2sc04511g |
Ejemplares similares
-
An evolutionary algorithm for the discovery of porous organic cages
por: Berardo, Enrico, et al.
Publicado: (2018) -
Systematic exploration of accessible topologies of cage molecules via minimalistic models
por: Tarzia, Andrew, et al.
Publicado: (2023) -
Deep generative design of porous organic cages via a variational autoencoder
por: Zhou, Jiajun, et al.
Publicado: (2023) -
Unlocking the computational design of metal–organic cages
por: Tarzia, Andrew, et al.
Publicado: (2022) -
Explainable graph neural networks for organic cages
por: Yuan, Qi, et al.
Publicado: (2022)