Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning
Abstract We use machine learning tools for the design and discovery of ABO3-type perovskite oxides for various energy applications, using over 7000 data points from the literature. We demonstrate a robust learning framework for efficient and accurate prediction of total conductivity of perovskites a...
Guardado en:
Autores principales: | Pikee Priya, N. R. Aluru |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/df540142d82c42e48254d9c8bb7bed48 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning for perovskite materials design and discovery
por: Qiuling Tao, et al.
Publicado: (2021) -
Accelerated discovery of a large family of quaternary chalcogenides with very low lattice thermal conductivity
por: Koushik Pal, et al.
Publicado: (2021) -
Designing polymer nanocomposites with high energy density using machine learning
por: Zhong-Hui Shen, et al.
Publicado: (2021) -
Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints
por: Achintha Ihalage, et al.
Publicado: (2021) -
Discovery of higher-order topological insulators using the spin Hall conductivity as a topology signature
por: Marcio Costa, et al.
Publicado: (2021)