Author

E. Rotenberg

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Preprint Aug 2026

Electronic and chemical phase identification in photoemission experiments using unsupervised machine learning

Vacuum ultraviolet photoemission spectroscopies are very information-rich experiments, but due to their surface sensitivity, data are often collected on an initially uncharacterized surface. Traditional raster-grid approaches for locating optimal measurement regions can be time-consuming. In this work, we introduce AAR...

Matthew C. Staab, Joseph Pandur, E. Rotenberg et al. · 2 citations

#machine learning Review Oct 2026

Progress and Prospect of AI in ARPES Workflow

This review introduces ML methods that are most relevant to experimentalists working in condensed matter physics and materials science and follows the ARPES workflow, reviewing existing ML applications at each step and discussing their advantages, limitations, and potential for future development.

S. Ekahana, Aalok Tiwari, Pratik Saud et al. · 0 citations