Cargando…
A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic heterogeneity remain elusive. Here, we demonstrate that novel...
Autores principales: | Wang, Qi, Jain, Anubhav |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6895099/ https://www.ncbi.nlm.nih.gov/pubmed/31804485 http://dx.doi.org/10.1038/s41467-019-13511-9 |
Ejemplares similares
-
Urban interstices : the aesthetics and the politics of the in-between
Publicado: (2013) -
Machine learning modeling for the prediction of plastic properties in metallic glasses
por: Amigo, Nicolás, et al.
Publicado: (2023) -
Energy of the Isolated Metastable Iron-Nickel FCC Nanocluster with a Carbon Atom in the Tetragonal Interstice
por: Bondarenko, Natalya V., et al.
Publicado: (2017) -
Macroscopic tensile plasticity by scalarizating stress distribution in bulk metallic glass
por: Gao, Meng, et al.
Publicado: (2016) -
Precisely Determining Ultralow level UO(2)(2+) in Natural Water with Plasmonic Nanowire Interstice Sensor
por: Gwak, Raekeun, et al.
Publicado: (2016)