Machine Learning for Optical Scanning Probe Nanoscopy

ORAL

Abstract

The ability to perform nanometer-scale optical imaging and spectroscopy is key to deciphering the low-energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Here, we would like to show that the s-SNOM, together with scanning probe research in general, can benefit in many ways from artificial intelligence (AI) and machine learning (ML) algorithms. Augmented with AI- and ML-enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

*The development of the machine learning used for polaritonic imaging is supported as part of the Energy Frontier Research Center on Programmable Quantum Materials funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award no. DE-SC0019443. The development of ML protocols at Columbia, Stony Brook, and Brookhaven is supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Co-design Center for Quantum Advantage (C2QA) under contract number DE-SC0012704. Mengkun Liu was partially supported by the RISE2 node of NASA's Solar System Exploration Research Virtual Institute under NASA Cooperative Agreement 80NSSC19MO2015. The Flatiron Institute is a Division of the Simons Foundation.

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Publication: arXiv:2204.09820

Presenters

  • Suheng Xu

    • Columbia University

Authors

  • Suheng Xu

    • Columbia University

  • Xinzhong Chen

    • Stony Brook University (SUNY)

  • Sara Shabani

    • Columbia University

  • Yueqi Zhao

    • UCSD

  • Matthew Fu

    • Columbia University

  • Andrew Millis

    • Columbia University
    • Columbia University, Flatiron Institute

  • Michael M Fogler

    • University of California, San Diego

  • Abhay N Pasupathy

    • Brookhaven National Laboratory & Columbia University
    • Columbia University

  • Mengkun Liu

    • Stony Brook University (SUNY)

  • Dmitri N Basov

    • Columbia University
    • Department of Physics, Columbia University, New York, NY, USA