Open AccessK-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy
Author(s) -
Charles Melton,
Marcus M. Noack,
Taisuke Ohta,
Thomas E. Beechem,
Jeremy T. Robinson,
Xiaotian Zhang,
Aaron Bostwick,
Chris Jozwiak,
Roland Koch,
Petrus H. Zwart,
Alexander Hexemer,
Eli Rotenberg
Publication year - 2020
Publication title -
machine learning: science and technology
Language(s) - English
Resource type - Journals
ISSN - 2632-2153
DOI - 10.1088/2632-2153/abab61
Subject(s) - angle resolved photoemission spectroscopy , gaussian , photoemission spectroscopy , gaussian process , cluster analysis , metric (unit) , kriging , data set , synchrotron , data collection , computer science , materials science , computational physics , physics , optics , x ray photoelectron spectroscopy , artificial intelligence , spectral line , mathematics , statistics , nuclear magnetic resonance , machine learning , operations management , quantum mechanics , astronomy , economics
We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.