Symmetry-aware recursive image similarity exploration for materials microscopy
Abstract In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific di...
Guardado en:
Autores principales: | Tri N. M. Nguyen, Yichen Guo, Shuyu Qin, Kylie S. Frew, Ruijuan Xu, Joshua C. Agar |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/928ea7d232ed406c8c9ab3b327b39c77 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Comparing crystal structures with symmetry and geometry
por: John C. Thomas, et al.
Publicado: (2021) -
Predicting stable crystalline compounds using chemical similarity
por: Hai-Chen Wang, et al.
Publicado: (2021) -
Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
por: Mark P. Oxley, et al.
Publicado: (2021) -
Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning
por: Leslie Ching Ow Tiong, et al.
Publicado: (2020) -
Differential programming enabled functional imaging with Lorentz transmission electron microscopy
por: Tao Zhou, et al.
Publicado: (2021)