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Chen X, Xu S, Shabani S, Zhao Y, Fu M, Millis AJ, Fogler MM, Pasupathy AN, Liu M, Basov DN. Machine Learning for Optical Scanning Probe Nanoscopy. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2109171. [PMID: 36333118 DOI: 10.1002/adma.202109171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 07/09/2022] [Indexed: 06/16/2023]
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 the scattering-type scanning near-field optical microscopy (s-SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown 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.
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Affiliation(s)
- Xinzhong Chen
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Suheng Xu
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Sara Shabani
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Yueqi Zhao
- Department of Physics, University of California at San Diego, La Jolla, CA, 92093-0319, USA
| | - Matthew Fu
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Andrew J Millis
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Michael M Fogler
- Department of Physics, University of California at San Diego, La Jolla, CA, 92093-0319, USA
| | - Abhay N Pasupathy
- Department of Physics, Columbia University, New York, NY, 10027, USA
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, 11794, USA
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - D N Basov
- Department of Physics, Columbia University, New York, NY, 10027, USA
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Zou Q, Oli BD, Zhang H, Benigno J, Li X, Li L. Deciphering Alloy Composition in Superconducting Single-Layer FeSe 1-xS x on SrTiO 3(001) Substrates by Machine Learning of STM/S Data. ACS APPLIED MATERIALS & INTERFACES 2023; 15:22644-22650. [PMID: 37125966 PMCID: PMC10176460 DOI: 10.1021/acsami.2c23324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, a plain visual analysis of STM images can be challenging for such determination in multicomponent alloys, particularly beyond the diluted limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify hidden patterns and correlations. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe1-xSx alloys epitaxially grown on SrTiO3(001) substrates via molecular beam epitaxy. First, the K-means clustering method is applied to identify defect-related dI/dV tunneling spectra taken by current imaging tunneling spectroscopy. Then, the Se/S ratio is calculated by analyzing the remaining spectra based on the singular value decomposition method. Such analysis provides an efficient and reliable determination of alloy composition and further reveals the correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films.
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Affiliation(s)
- Qiang Zou
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506, United States
| | - Basu Dev Oli
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506, United States
| | - Huimin Zhang
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506, United States
| | - Joseph Benigno
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506, United States
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, West Virginia 26506, United States
| | - Lian Li
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia 26506, United States
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Chakraborty D, Black-Schaffer AM. Quasiparticle Interference as a Direct Experimental Probe of Bulk Odd-Frequency Superconducting Pairing. PHYSICAL REVIEW LETTERS 2022; 129:247001. [PMID: 36563253 DOI: 10.1103/physrevlett.129.247001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 09/09/2022] [Accepted: 10/25/2022] [Indexed: 06/17/2023]
Abstract
We show that quasiparticle interference (QPI) due to omnipresent weak impurities and probed by Fourier transform scanning tunneling microscopy and spectroscopy acts as a direct experimental probe of bulk odd-frequency superconducting pairing. Taking the example of a conventional s-wave superconductor under applied magnetic field, we show that the nature of the QPI peaks can only be characterized by including the odd-frequency pairing correlations generated in this system. In particular, we identify that the defining feature of odd-frequency pairing gives rise to a bias asymmetry in the QPI, present generically in materials with odd-frequency pairing irrespective of its origin.
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Affiliation(s)
- Debmalya Chakraborty
- Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden
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