Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning
Experimental search for high-temperature ferroelectric perovskites is challenging due to the vast chemical space and lack of predictive guidelines. Here the authors demonstrate a two-step machine learning approach to sequentially guide experiments in search of promising perovskites with high ferroel...
Guardado en:
Autores principales: | Prasanna V. Balachandran, Benjamin Kowalski, Alp Sehirlioglu, Turab Lookman |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2018
|
Materias: | |
Acceso en línea: | https://doaj.org/article/cc660229b2ca4dd09848e4c55c3589a5 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Accelerated search for materials with targeted properties by adaptive design
por: Dezhen Xue, et al.
Publicado: (2016) -
Learning from data to design functional materials without inversion symmetry
por: Prasanna V. Balachandran, et al.
Publicado: (2017) -
Recipes for improper ferroelectricity in molecular perovskites
por: Hanna L. B. Boström, et al.
Publicado: (2018) -
Harnessing machine learning to guide phylogenetic-tree search algorithms
por: Dana Azouri, et al.
Publicado: (2021) -
Three-dimensional imaging of vortex structure in a ferroelectric nanoparticle driven by an electric field
por: D. Karpov, et al.
Publicado: (2017)