Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T60: Emerging Trends in Molecular Dynamics Simulations and Machine Learning VI
11:30 AM–1:54 PM,
Thursday, March 9, 2023
Room: Room 419
Sponsoring
Unit:
DCOMP
Chair: Pankaj Rajak, Argonne National Laboratory
Abstract: T60.00009 : Interpretable machine learning analysis of quantum gas microscopy data of doped Fermi-Hubbard model*
1:06 PM–1:18 PM
Presenter:
YANJUN LIU
(Cornell University)
Authors:
YANJUN LIU
(Cornell University)
Yao Wang
(Clemson University)
Henning Schloemer
(Ludwig-Maximilians-Universitaet (LMU-Munich))
Annabelle Bohrdt
(Harvard University and ITAMP)
Timon A Hilker
(Max Planck Institute of Quantum Optics)
Fabian Grusdt
(LMU Munich)
Immanuel Bloch
(Max Planck Institute for Quantum Optics)
Eun-Ah Kim
(Cornell University)
Joannis Koepsell
(Max Planck Institute of Quantum Optics)
Dominik Bourgund
(Max Planck Institute of Quantum Optics)
Sarah Hirthe
(Max Planck Institute of Quantum Optics)
Guillaume Salomon
(Max Planck Institute for Plasma Physics)
Christian Gross
(Max Planck Institute for Plasma Physics)
Pimonpan Sompet
(Max Planck Institute for Quantum Optics)
[1] Miles, C., Bohrdt, A., Wu, R. et al. Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data. Nat Commun 12, 3905 (2021). https://doi.org/10.1038/s41467-021-23952-w
[2] Koepsell, Joannis, et al. "Microscopic evolution of doped Mott insulators from polaronic metal to Fermi liquid." Science 374.6563 (2021): 82-86.
*This research is funded in part by the Gordon and Betty Moore Foundation's EPiQS Initiative, Grant GBMF10436 to E-AK.
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