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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| 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 |