Predicting synthesizability of crystalline materials via deep learning
Predicting the synthesizability of unknown crystals is important for accelerating materials discovery. Here, the synthesizability of crystals with any given composition and structure can be predicted by a deep learning model that maps crystals onto color-coded 3D images processed by convolutional ne...
Guardado en:
Autores principales: | Ali Davariashtiyani, Zahra Kadkhodaie, Sara Kadkhodaei |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/286e3c14437f415882db24b057ccb59c |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
StressNet - Deep learning to predict stress with fracture propagation in brittle materials
por: Yinan Wang, et al.
Publicado: (2021) -
Virtual experimentations by deep learning on tangible materials
por: Takashi Honda, et al.
Publicado: (2021) -
Reducing crystallinity in solid polymer electrolytes for lithium-metal batteries via statistical copolymerization
por: Vincent St-Onge, et al.
Publicado: (2021) -
High performance crystalline nanocellulose using an ancestral endoglucanase
por: Borja Alonso-Lerma, et al.
Publicado: (2020) -
Controlled growth and ordering of poorly-crystalline calcium-silicate-hydrate nanosheets
por: Felipe Basquiroto de Souza, et al.
Publicado: (2021)