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CPP 104: Topical Session: Data Driven Materials Science - Machine Learning Applications (joint session MM/CPP)
CPP 104.6: Vortrag
Donnerstag, 19. März 2020, 18:45–19:00, BAR 205
A computational route between band mapping and band structure — R. Patrick Xian1, •Vincent Stimper2, Marios Zacharias1, Shuo Dong1, Maciej Dendzik1, Samuel Beaulieu1, Matthias Scheffler1, Bernhard Schölkopf2, Martin Wolf1, Laurenz Rettig1, Christian Carbogno1, Stefan Bauer2, and Ralph Ernstorfer1 — 1Fritz Haber Institute of the Max Planck Society, Berlin, Germany — 2Max Planck Institute for Intelligent Systems, Tübingen, Germany
In solid state physics, the electronic band structure imprints the multidimensional relations between energy and momentum of periodically confined electrons. Photoemission spectroscopy has provided a reliable source of experimental validation for electronic structure theory and the understanding of electronic properties. Recent advancements in UV sources and detector technology leads to the growth of band mapping data in resolution, size and scale. This offers fresh opportunities for high-throughput material characterization. Here, we present an efficient probabilistic machine learning framework to connect photoemission band mapping with band structure calculations by reconstructing the full 3D valence band structure of tungsten diselenide within its first Brillouin zone. We then digitize the empirical shape of the reconstructed bands using orthonormal bases for multiscale sampling and comparison with theory. Our approaches open up avenues for the precise quantification of band structures and realize a viable path integrating band mapping data with computational materials science databases.