Cargando…
Learning motifs and their hierarchies in atomic resolution microscopy
Characterizing materials to atomic resolution and first-principles structure-property prediction are two pillars for accelerating functional materials discovery. However, we are still lacking a rapid, noise-robust framework to extract multilevel atomic structural motifs from complex materials to com...
Autores principales: | Dan, Jiadong, Zhao, Xiaoxu, Ning, Shoucong, Lu, Jiong, Loh, Kian Ping, He, Qian, Loh, N. Duane, Pennycook, Stephen J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9007509/ https://www.ncbi.nlm.nih.gov/pubmed/35417228 http://dx.doi.org/10.1126/sciadv.abk1005 |
Ejemplares similares
-
A multiscale generative model to understand disorder in domain boundaries
por: Dan, Jiadong, et al.
Publicado: (2023) -
Sub-angstrom noninvasive imaging of atomic arrangement in 2D hybrid perovskites
por: Telychko, Mykola, et al.
Publicado: (2022) -
High resolution atomic force and Kelvin probe force microscopy image data of InAs(001) surface using frequency modulation method
por: Park, Young Min, et al.
Publicado: (2020) -
Hierarchy in materials for maximized efficiency
por: Chen, Li-Hua, et al.
Publicado: (2020) -
Hierarchy: enhancing performances beyond limits
por: Sanchez, Clément
Publicado: (2020)