Predicting structure zone diagrams for thin film synthesis by generative machine learning
Controlling the microstructure of thin films is vital for tuning their properties. Here, machine learning is applied to obtain synthesis-composition-microstructure relationships in the form of structure zone diagrams for thin films, enabling microstructure prediction.
Guardado en:
Autores principales: | Lars Banko, Yury Lysogorskiy, Dario Grochla, Dennis Naujoks, Ralf Drautz, Alfred Ludwig |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b761d7313f9244cb9fc3e14a15558671 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep learning for visualization and novelty detection in large X-ray diffraction datasets
por: Lars Banko, et al.
Publicado: (2021) -
Flexible organic thin-film transistor immunosensor printed on a one-micron-thick film
por: Tsukuru Minamiki, et al.
Publicado: (2021) -
Machine-learned interatomic potentials for alloys and alloy phase diagrams
por: Conrad W. Rosenbrock, et al.
Publicado: (2021) -
Flexoelectric nanodomains in rare-earth iron garnet thin films under strain gradient
por: Hiroyasu Yamahara, et al.
Publicado: (2021) -
The effect of charge transfer transition on the photostability of lanthanide-doped indium oxide thin-film transistors
por: Penghui He, et al.
Publicado: (2021)