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Pattern Recognition with Deep Learning in Quantum Materials

ORAL

Abstract

The capabilities of surface probe experiments are rapidly expanding, providing views on quantum materials at unprecedented length and time scales. Many such materials display intricate pattern formation in the electronic properties on the observable surface. This rich spatial information contains information about interactions, dimensionality, and disorder.[1] A well-tuned machine learning framework can decipher this information with minimal effort from the user.[2] We show the effectiveness of our deep learning framework on simulations of statistical models. We then use our machine learning model to analyze experimental data from an optical microscope[3] on a vanadium dioxide film as it goes through the insulator-metal transition.
[1] B. Phillabaum, et al. Nat Commun 3, 915(2012).
[2] L. Burzawa, et al. Phys. Rev. Materials 3, 033805(2019).
[3] A. Zimmers, et al. Phys. Rev. Lett. 110, 056601(2013).

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Presenters

  • Sayan Basak

    Dept. of Physics and Astronomy, Purdue University

Authors

  • Sayan Basak

    Dept. of Physics and Astronomy, Purdue University

  • Forrest Simmons

    Dept. of Physics and Astronomy, Purdue University, Purdue University

  • Pavel Salev

    Dept. of Physics and Center for Advanced Nanoscience, UCSD, La Jolla, CA, USA, Department of Physics, University of California, San Diego, University of California, San Diego

  • Ivan Schuller

    University of California, San Diego, Dept. of Physics and Center for Advanced Nanoscience, UCSD, La Jolla, CA, USA, Physics Department, University of California, San Diego, Department of Physics, University of California, San Diego

  • Lionel Aigouy

    LPEM, ESPCI-PSL, CNRS, Sorbonne Univ., Paris-France

  • Alexandre J Zimmers

    LPEM, ESPCI-PSL, CNRS, Sorbonne Univ., Paris-France

  • Erica W Carlson

    Dept. of Physics and Astronomy, Purdue University, Purdue University