Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of...

Descripción completa

Detalles Bibliográficos
Autores principales: Bang, Kihoon, Yeo, Byung Chul, Kim, Donghun, Han, Sang Soo, Lee, Hyuck Mo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173009/
https://www.ncbi.nlm.nih.gov/pubmed/34078997
http://dx.doi.org/10.1038/s41598-021-91068-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173009/
https://www.ncbi.nlm.nih.gov/pubmed/34078997
http://dx.doi.org/10.1038/s41598-021-91068-8

Ejemplares similares