Virtual experimentations by deep learning on tangible materials
Artificial intelligence may significantly accelerate the discovery of new materials but is not easily applicable to non-periodic structures. Here, a deep learning framework is proposed to predict properties of tangible carbon nanotubes by generating virtual structures at different scales and composi...
Guardado en:
Autores principales: | Takashi Honda, Shun Muroga, Hideaki Nakajima, Taiyo Shimizu, Kazufumi Kobashi, Hiroshi Morita, Toshiya Okazaki, Kenji Hata |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/1f287afbde7e4b7b8103d31f93afd816 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Predicting synthesizability of crystalline materials via deep learning
por: Ali Davariashtiyani, et al.
Publicado: (2021) -
StressNet - Deep learning to predict stress with fracture propagation in brittle materials
por: Yinan Wang, et al.
Publicado: (2021) -
The role of valve stiffness in the insurgence of deep vein thrombosis
por: Zoe Schofield, et al.
Publicado: (2020) -
Charged domain boundaries stabilized by translational symmetry breaking in the hybrid improper ferroelectric Ca3–x Sr x Ti2O7
por: Hiroshi Nakajima, et al.
Publicado: (2021) -
Calcium-free double-layered cuprate superconductors with critical temperature above 100 K
por: Hiroki Ninomiya, et al.
Publicado: (2021)