Automatic extraction of fine structural information in angle-resolved photoemission spectroscopy by multi-stage clustering algorithm
Abstract Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis.
Authors
- Pavel Dudin (ORCID: https://orcid.org/0000-0002-5971-0395)
- Jiaou Wang (ORCID: https://orcid.org/0000-0002-4686-1821)
- Zhen Zhang (ORCID: https://orcid.org/0000-0001-6966-0208)
- Y. P. Huang (ORCID: https://orcid.org/0009-0005-6128-0936)
- J. Ávila (ORCID: https://orcid.org/0000-0003-1027-5676)
- Yuhui Dong (ORCID: https://orcid.org/0000-0002-4422-4685)
- Yi Zhang (ORCID: https://orcid.org/0000-0002-6342-4228)
- Zhesheng Chen (ORCID: https://orcid.org/0000-0001-7978-5637)
- Xinyu Pan (ORCID: https://orcid.org/0000-0002-2116-8799)
- L. Bian (ORCID: https://orcid.org/0000-0001-5209-5097)
- Chen Liu (ORCID: https://orcid.org/0009-0000-7349-8610)
Institutions
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Synchrotron soleil (FR)
- Nanjing University of Science and Technology (CN)
- Institute of High Energy Physics (CN)
- National Synchrotron Radiation Laboratory (CN)
Publication Details
- Journal
- Communications Physics
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1038/s42005-024-01878-1
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Key Research and Development Program of China
- High Energy Photon Source