| Autor: |
Maria El Abbassi, Jan Overbeck, Oliver Braun, Michel Calame, Herre S. J. van der Zant, Mickael L. Perrin |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Communications Physics, Vol 4, Iss 1, Pp 1-9 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2399-3650 |
| DOI: |
10.1038/s42005-021-00549-9 |
| Popis: |
In the field of nanoscience, clustering methods have gained momentum for the analysis of experimental datasets with the aim of uncovering new physical properties. Here, the authors describe an unsupervised machine learning methodology that selects the optimal combination of feature space, clustering method, and number of clusters for the analysis of a range of experimental datasets, including break-junction traces, I-V curves, and Raman spectra. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
