Quantitative prediction of grain boundary thermal conductivities from local atomic environments
Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries.
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2020
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/c7fe7ca741a14e9ea17673a391810703 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries. |
|---|