A machine learning route between band mapping and band structure

Xian, R. Patrick; Stimper, Vincent; Zacharias, Marios; Dendzik, Maciej Ramon; Dong, Shuo; Beaulieu, Samuel; Schölkopf, Bernhard; Wolf, Martin; Rettig, Laurenz; Carbogno, Christian; Bauer, Stefan; Ernstorfer, Ralph

doi:10.1038/s43588-022-00382-2

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A machine learning route between band mapping and band structure

MPG-Autoren

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Xian, R. Patrick
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

Stimper, Vincent
Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons246539

Zacharias, Marios
NOMAD, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons205838

Dendzik, Maciej Ramon
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons206871

Dong, Shuo
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons227651

Beaulieu, Samuel
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

Schölkopf, Bernhard
Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons22250

Wolf, Martin
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons104701

Rettig, Laurenz
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons21413

Carbogno, Christian
NOMAD, Fritz Haber Institute, Max Planck Society;

Bauer, Stefan
Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons21497

Ernstorfer, Ralph
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

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Zitation

Xian, R. P., Stimper, V., Zacharias, M., Dendzik, M. R., Dong, S., Beaulieu, S., et al. (2023). A machine learning route between band mapping and band structure. Nature Computational Science, 3(1), 101-114. doi:10.1038/s43588-022-00382-2.

Zitierlink: https://hdl.handle.net/21.11116/0000-0006-8E10-8

Zusammenfassung

The electronic band structure (BS) of solid state materials imprints the
multidimensional and multi-valued functional relations between energy and
momenta of periodically confined electrons. Photoemission spectroscopy is a
powerful tool for its comprehensive characterization. A common task in
photoemission band mapping is to recover the underlying quasiparticle
dispersion, which we call band structure reconstruction. Traditional methods
often focus on specific regions of interests yet require extensive human
oversight. To cope with the growing size and scale of photoemission data, we
develop a generic machine-learning approach leveraging the information within
electronic structure calculations for this task. We demonstrate its capability
by reconstructing all fourteen valence bands of tungsten diselenide and
validate the accuracy on various synthetic data. The reconstruction uncovers
previously inaccessible momentum-space structural information on both global
and local scales in conjunction with theory, while realizing a path towards
integrating band mapping data into materials science databases.