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Fu M, Xu S, Zhang S, Ruta FL, Pack J, Mayer RA, Chen X, Moore SL, Rizzo DJ, Jessen BS, Cothrine M, Mandrus DG, Watanabe K, Taniguchi T, Dean CR, Pasupathy AN, Bisogni V, Schuck PJ, Millis AJ, Liu M, Basov DN. Accelerated Nano-Optical Imaging through Sparse Sampling. NANO LETTERS 2024; 24:2149-2156. [PMID: 38329715 DOI: 10.1021/acs.nanolett.3c03733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
The integration time and signal-to-noise ratio are inextricably linked when performing scanning probe microscopy based on raster scanning. This often yields a large lower bound on the measurement time, for example, in nano-optical imaging experiments performed using a scanning near-field optical microscope (SNOM). Here, we utilize sparse scanning augmented with Gaussian process regression to bypass the time constraint. We apply this approach to image charge-transfer polaritons in graphene residing on ruthenium trichloride (α-RuCl3) and obtain key features such as polariton damping and dispersion. Critically, nano-optical SNOM imaging data obtained via sparse sampling are in good agreement with those extracted from traditional raster scans but require 11 times fewer sampled points. As a result, Gaussian process-aided sparse spiral scans offer a major decrease in scanning time.
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Affiliation(s)
- Matthew Fu
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Suheng Xu
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Shuai Zhang
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Francesco L Ruta
- Department of Physics, Columbia University, New York, New York 10027, United States
- Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York 10027, United States
| | - Jordan Pack
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Rafael A Mayer
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Xinzhong Chen
- Department of Physics, Columbia University, New York, New York 10027, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Samuel L Moore
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Daniel J Rizzo
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Bjarke S Jessen
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Matthew Cothrine
- Department of Material Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - David G Mandrus
- Material Science & Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
- Department of Material Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Kenji Watanabe
- Research Center for Electronic and Optical Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
| | - Takashi Taniguchi
- Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
| | - Cory R Dean
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Abhay N Pasupathy
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Valentina Bisogni
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - P James Schuck
- Department of Mechanical Engineering, Columbia University, New York, New York 10027, United States
| | - Andrew J Millis
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
- National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, New York 11973, United States
| | - D N Basov
- Department of Physics, Columbia University, New York, New York 10027, United States
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Hoxie DJ, Bangalore PV, Appavoo K. Machine learning of all-dielectric core-shell nanostructures: the critical role of the objective function in inverse design. NANOSCALE 2023; 15:19203-19212. [PMID: 37982436 DOI: 10.1039/d3nr04392d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
To integrate nanophotonics into light-based technologies, it is critical to elicit a desired optical response from its fundamental component, a nanoresonator. Because the optical resonance of a nanoresonator depends strongly on its base material and structural features, machine learning has been contemplated to enhance the design and optimization processes. However, its accuracy in searching the vast parameter space of nanophotonics still poses unresolved questions. Here, we show how the choice of objective functions, in combination with trained neural networks, can drastically change the optimization process-even for a simple nanophotonic structure. To assess how different objective functions select the correct structural parameters that generate a desired optical Mie response, we use a simple core-shell, all-dielectric nanostructure as the benchmark. By controlling the proportion of training data, which represents the "experience" level, we also quantify how the various objective functions perform in finding the ground-truth parameters. Our findings demonstrate that certain objective functions exhibit improved accuracy when used with highly "experienced" neural networks. Surprisingly, we also find other objective functions that perform better when paired with less "experienced" neural networks. Taken together, our results emphasize that it is critical to understand how neural networks are coupled to optimization schemes, as is evident even when a simple core-shell nanostructure is used.
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Affiliation(s)
- David J Hoxie
- Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Purushotham V Bangalore
- Department. of Computer Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kannatassen Appavoo
- Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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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: 6] [Impact Index Per Article: 6.0] [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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Ko W, Gai Z, Puretzky AA, Liang L, Berlijn T, Hachtel JA, Xiao K, Ganesh P, Yoon M, Li AP. Understanding Heterogeneities in Quantum Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022:e2106909. [PMID: 35170112 DOI: 10.1002/adma.202106909] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Quantum materials are usually heterogeneous, with structural defects, impurities, surfaces, edges, interfaces, and disorder. These heterogeneities are sometimes viewed as liabilities within conventional systems; however, their electronic and magnetic structures often define and affect the quantum phenomena such as coherence, interaction, entanglement, and topological effects in the host system. Therefore, a critical need is to understand the roles of heterogeneities in order to endow materials with new quantum functions for energy and quantum information science applications. In this article, several representative examples are reviewed on the recent progress in connecting the heterogeneities to the quantum behaviors of real materials. Specifically, three intertwined topic areas are assessed: i) Reveal the structural, electronic, magnetic, vibrational, and optical degrees of freedom of heterogeneities. ii) Understand the effect of heterogeneities on the behaviors of quantum states in host material systems. iii) Control heterogeneities for new quantum functions. This progress is achieved by establishing the atomistic-level structure-property relationships associated with heterogeneities in quantum materials. The understanding of the interactions between electronic, magnetic, photonic, and vibrational states of heterogeneities enables the design of new quantum materials, including topological matter and quantum light emitters based on heterogenous 2D materials.
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Affiliation(s)
- Wonhee Ko
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Zheng Gai
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Alexander A Puretzky
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Liangbo Liang
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Tom Berlijn
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Jordan A Hachtel
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Kai Xiao
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Panchapakesan Ganesh
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - Mina Yoon
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
| | - An-Ping Li
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA
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