Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning
Abstract 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) pat...
Guardado en:
Autores principales: | Kihoon Bang, Byung Chul Yeo, Donghun Kim, Sang Soo Han, Hyuck Mo Lee |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/259d11e4579848368d87ec0c8dd33891 |
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