Unsupervised learning of spatially-resolved ARPES spectra for epitaxially grown graphene via non-negative matrix factorization
Abstract This study proposed an unsupervised machine-learning approach for analyzing spatially-resolved ARPES. A combination of non-negative matrix factorization (NMF) and k-means clustering was applied to spatially-resolved ARPES spectra of the graphene epitaxially grown on a SiC substrate. The Dir...
Furkejuvvon:
| Váldodahkkit: | , |
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| Materiálatiipa: | Artigo |
| Giella: | Inglês |
| Almmustuhtton: |
Nature Portfolio
2024-10-01
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| Ráidu: | Scientific Reports |
| Fáttát: | |
| Liŋkkat: | https://doi.org/10.1038/s41598-024-73795-w |
| Fáddágilkorat: |
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