Identifying superionic conductors by materials informatics and high-throughput synthesis
High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.
Guardado en:
Autores principales: | Masato Matsubara, Akitoshi Suzumura, Nobuko Ohba, Ryoji Asahi |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/37c9bea1495d4ca7b93080fed99149bd |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Rupture stress of liquid metal nanoparticles and their applications in stretchable conductors and dielectrics
por: Yang Liu, et al.
Publicado: (2021) -
Design of a multifunctional polar metal via first-principles high-throughput structure screening
por: Yue-Wen Fang, et al.
Publicado: (2020) -
A practical guide to promote informatics-driven efficient biotopographic material development
por: Yuanlong Guo, et al.
Publicado: (2022) -
Fully solution processed liquid metal features as highly conductive and ultrastretchable conductors
por: Hangyu Zhu, et al.
Publicado: (2021) -
A stretchable and adhesive ionic conductor based on polyacrylic acid and deep eutectic solvents
por: Gang Li, et al.
Publicado: (2021)