1
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Pregowska A, Roszkiewicz A, Osial M, Giersig M. How scanning probe microscopy can be supported by artificial intelligence and quantum computing? Microsc Res Tech 2024; 87:2515-2539. [PMID: 38864463 DOI: 10.1002/jemt.24629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024]
Abstract
The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.
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Affiliation(s)
- Agnieszka Pregowska
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Agata Roszkiewicz
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Magdalena Osial
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
| | - Michael Giersig
- Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland
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2
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Arnold J, Schäfer F, Edelman A, Bruder C. Mapping Out Phase Diagrams with Generative Classifiers. PHYSICAL REVIEW LETTERS 2024; 132:207301. [PMID: 38829098 DOI: 10.1103/physrevlett.132.207301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/10/2023] [Accepted: 03/18/2024] [Indexed: 06/05/2024]
Abstract
One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states.
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Affiliation(s)
- Julian Arnold
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
| | - Frank Schäfer
- CSAIL, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Alan Edelman
- CSAIL, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Christoph Bruder
- Department of Physics, University of Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland
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3
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Kang S, Park J, Lee M. Machine learning-enabled autonomous operation for atomic force microscopes. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:123704. [PMID: 38109471 DOI: 10.1063/5.0172682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/26/2023] [Indexed: 12/20/2023]
Abstract
The use of scientific instruments generally requires prior knowledge and skill on the part of operators, and thus, the obtained results often vary with different operators. The autonomous operation of instruments producing reproducible and reliable results with little or no operator-to-operator variation could be of considerable benefit. Here, we demonstrate the autonomous operation of an atomic force microscope using a machine learning-based object detection technique. The developed atomic force microscope was able to autonomously perform instrument initialization, surface imaging, and image analysis. Two cameras were employed, and a machine-learning algorithm of region-based convolutional neural networks was implemented, to detect and recognize objects of interest and to perform self-calibration, alignment, and operation of each part of the instrument, as well as the analysis of obtained images. Our machine learning-based approach could be generalized to apply to various types of scanning probe microscopes and other scientific instruments.
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Affiliation(s)
- Seongseok Kang
- Department of Physics, Chungbuk National University, Seowon-Gu, Cheongju 28644, South Korea
| | - Junhong Park
- Department of Physics, Chungbuk National University, Seowon-Gu, Cheongju 28644, South Korea
| | - Manhee Lee
- Department of Physics, Chungbuk National University, Seowon-Gu, Cheongju 28644, South Korea
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4
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Gui C, Zhang Z, Li Z, Luo C, Xia J, Wu X, Chu J. Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials. iScience 2023; 26:107982. [PMID: 37810254 PMCID: PMC10551659 DOI: 10.1016/j.isci.2023.107982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
Defects are prevalent in two-dimensional (2D) materials due to thermal equilibrium and processing kinetics. The presence of various defect types can affect material properties significantly. With the development of the advanced transmission electron microscopy (TEM), the property-related structures could be investigated in multiple dimensions. It produces TEM datasets containing a large amount of information. Traditional data analysis is influenced by the subjectivity of researchers, and manual analysis is inefficient and imprecise. Recent developments in deep learning provide robust methods for the quantitative identification of defects in 2D materials efficiently and precisely. Taking advantage of big data, it breaks the limitations of TEM as a local characterization tool, making TEM an intelligent macroscopic analysis method. In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. Initially, an in-depth examination of the distinctions between TEM and natural images is presented. Subsequently, a comprehensive exploration of TEM data analysis ensues, encompassing denoising, point defects, line defects, planar defects, quantitative analysis, and applications. Furthermore, an exhaustive assessment of the significant obstacles encountered in the accurate identification of distinct structures is also provided.
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Affiliation(s)
- Chen Gui
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
| | - Zhihao Zhang
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
| | - Zongyi Li
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China
| | - Chen Luo
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
| | - Jiang Xia
- JCET SEMICONDUCTOR INTEGRATION (SHAOXING) CO, LTD, Shaoxing, Zhejiang 312000, China
| | - Xing Wu
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
| | - Junhao Chu
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai 200241, China
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
- Institute of Optoelectronics, Fudan University, Shanghai 200433, China. Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
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5
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Sobral JA, Obernauer S, Turkel S, Pasupathy AN, Scheurer MS. Machine learning the microscopic form of nematic order in twisted double-bilayer graphene. Nat Commun 2023; 14:5012. [PMID: 37591848 PMCID: PMC10435506 DOI: 10.1038/s41467-023-40684-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/01/2023] [Indexed: 08/19/2023] Open
Abstract
Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.
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Affiliation(s)
- João Augusto Sobral
- Institute for Theoretical Physics III, University of Stuttgart, 70550, Stuttgart, Germany.
- Institute for Theoretical Physics, University of Innsbruck, A-6020, Innsbruck, Austria.
| | - Stefan Obernauer
- Institute for Theoretical Physics, University of Innsbruck, A-6020, Innsbruck, Austria
| | - Simon Turkel
- Department of Physics, Columbia University, 10027, New York, NY, USA
| | - Abhay N Pasupathy
- Department of Physics, Columbia University, 10027, New York, NY, USA
- Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, 11973, Upton, NY, USA
| | - Mathias S Scheurer
- Institute for Theoretical Physics III, University of Stuttgart, 70550, Stuttgart, Germany
- Institute for Theoretical Physics, University of Innsbruck, A-6020, Innsbruck, Austria
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6
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Jang Y, Kim CH, Go A. Classification of magnetic order from electronic structure by using machine learning. Sci Rep 2023; 13:12445. [PMID: 37528106 PMCID: PMC10394061 DOI: 10.1038/s41598-023-38863-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/16/2023] [Indexed: 08/03/2023] Open
Abstract
Identifying the magnetic state of materials is of great interest in a wide range of applications, but direct identification is not always straightforward due to limitations in neutron scattering experiments. In this work, we present a machine-learning approach using decision-tree algorithms to identify magnetism from the spin-integrated excitation spectrum, such as the density of states. The dataset was generated by Hartree-Fock mean-field calculations of candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from first-principle calculations targeting BaOsO[Formula: see text]. Our machine learning model was trained using various types of spectral data, including local density of states, momentum-resolved density of states at high-symmetry points, and the lowest excitation energies from the Fermi level. Although the density of states shows good performance for machine learning, the broadening method had a significant impact on the model's performance. We improved the model's performance by designing the excitation energy as a feature for machine learning, resulting in excellent classification of antiferromagnetic order, even for test samples generated by different methods from the training samples used for machine learning.
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Affiliation(s)
- Yerin Jang
- Department of Physics, Chonnam National University, Gwangju, 61186, Korea
| | - Choong H Kim
- Center for Correlated Electron Systems, Institute for Basic Science, Seoul, 08826, Korea.
- Department of Physics and Astronomy, Seoul National University, Seoul, 08826, Korea.
| | - Ara Go
- Department of Physics, Chonnam National University, Gwangju, 61186, Korea.
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7
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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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8
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Tang B, Song Y, Qin M, Tian Y, Wu ZW, Jiang Y, Cao D, Xu L. Machine learning-aided atomic structure identification of interfacial ionic hydrates from AFM images. Natl Sci Rev 2023; 10:nwac282. [PMID: 37266561 PMCID: PMC10232042 DOI: 10.1093/nsr/nwac282] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 06/21/2024] Open
Abstract
Relevant to broad applied fields and natural processes, interfacial ionic hydrates have been widely studied by using ultrahigh-resolution atomic force microscopy (AFM). However, the complex relationship between the AFM signal and the investigated system makes it difficult to determine the atomic structure of such a complex system from AFM images alone. Using machine learning, we achieved precise identification of the atomic structures of interfacial water/ionic hydrates based on AFM images, including the position of each atom and the orientations of water molecules. Furthermore, it was found that structure prediction of ionic hydrates can be achieved cost-effectively by transfer learning using neural network trained with easily available interfacial water data. Thus, this work provides an efficient and economical methodology that not only opens up avenues to determine atomic structures of more complex systems from AFM images, but may also help to interpret other scientific studies involving sophisticated experimental results.
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Affiliation(s)
- Binze Tang
- International Center for Quantum Materials, Peking University, Beijing100871, China
- School of Physics, Peking University, Beijing100871, China
| | - Yizhi Song
- International Center for Quantum Materials, Peking University, Beijing100871, China
- School of Physics, Peking University, Beijing100871, China
| | - Mian Qin
- School of Physics, Peking University, Beijing100871, China
| | - Ye Tian
- International Center for Quantum Materials, Peking University, Beijing100871, China
- School of Physics, Peking University, Beijing100871, China
| | - Zhen Wei Wu
- Institute of Nonequilibrium Systems, School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Ying Jiang
- International Center for Quantum Materials, Peking University, Beijing100871, China
- School of Physics, Peking University, Beijing100871, China
- Collaborative Innovation Center of Quantum Matter, Beijing100871, China
- CAS Center for Excellence in Topological Quantum Computation, University of Chinese Academy of Sciences, Beijing 100049, China
- Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials, Peking University, Beijing100871, China
| | - Duanyun Cao
- Beijing Key Laboratory of Environmental Science and Engineering, School of Materials Science and Engineering, Beijing Institute of Technology, Beijing100081, China
- Beijing Institute of Technology Chongqing Innovation Center, Chongqing401120, China
| | - Limei Xu
- International Center for Quantum Materials, Peking University, Beijing100871, China
- School of Physics, Peking University, Beijing100871, China
- Collaborative Innovation Center of Quantum Matter, Beijing100871, China
- Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials, Peking University, Beijing100871, China
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9
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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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10
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Cassella G, Sutterud H, Azadi S, Drummond ND, Pfau D, Spencer JS, Foulkes WMC. Discovering Quantum Phase Transitions with Fermionic Neural Networks. PHYSICAL REVIEW LETTERS 2023; 130:036401. [PMID: 36763402 DOI: 10.1103/physrevlett.130.036401] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 11/18/2022] [Indexed: 06/18/2023]
Abstract
Deep neural networks have been very successful as highly accurate wave function Ansätze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such Ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density, despite being given no a priori knowledge that a phase transition exists.
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Affiliation(s)
- Gino Cassella
- Department of Physics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Halvard Sutterud
- Department of Physics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Sam Azadi
- Department of Physics, University of Oxford, Oxford OX1 3PU, United Kingdom
| | - N D Drummond
- Department of Physics, Lancaster University, Lancaster LA1 4YB, United Kingdom
| | - David Pfau
- Department of Physics, Imperial College London, London SW7 2AZ, United Kingdom
- DeepMind, London N1C 4DJ, United Kingdom
| | | | - W M C Foulkes
- Department of Physics, Imperial College London, London SW7 2AZ, United Kingdom
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11
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Yu LW, Zhang SY, Shen PX, Deng DL. Unsupervised Learning of Interacting Topological Phases from Experimental Observables. FUNDAMENTAL RESEARCH 2023. [DOI: 10.1016/j.fmre.2022.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
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12
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Andrejevic N, Andrejevic J, Bernevig BA, Regnault N, Han F, Fabbris G, Nguyen T, Drucker NC, Rycroft CH, Li M. Machine-Learning Spectral Indicators of Topology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2204113. [PMID: 36193763 DOI: 10.1002/adma.202204113] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/18/2022] [Indexed: 06/16/2023]
Abstract
Topological materials discovery has emerged as an important frontier in condensed matter physics. While theoretical classification frameworks have been used to identify thousands of candidate topological materials, experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique sensitive to atoms' local symmetry and chemical bonding, which are intimately linked to band topology by the theory of topological quantum chemistry (TQC). Moreover, as a local structural probe, XAS is known to have high quantitative agreement between experiment and calculation, suggesting that insights from computational spectra can effectively inform experiments. In this work, computed X-ray absorption near-edge structure (XANES) spectra of more than 10 000 inorganic materials to train a neural network (NN) classifier that predicts topological class directly from XANES signatures, achieving F1 scores of 89% and 93% for topological and trivial classes, respectively is leveraged. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine-learning-augmented XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials, and may further inform field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.
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Affiliation(s)
- Nina Andrejevic
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA
- Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jovana Andrejevic
- Department of Physics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - B Andrei Bernevig
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Department of Physics, Princeton University, Princeton, NJ, 08544, USA
- Donostia International Physics Center, P. Manuel de Lardizabal 4, Donostia-San Sebastian, 20018, Spain
- IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, Bilbao, 48009, Spain
| | - Nicolas Regnault
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Department of Physics, Princeton University, Princeton, NJ, 08544, USA
| | - Fei Han
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA
- Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, Bilbao, 48009, Spain
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Gilberto Fabbris
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Thanh Nguyen
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA
- Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, Bilbao, 48009, Spain
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Nathan C Drucker
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA
- Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Physics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Chris H Rycroft
- Department of Physics, University of Pennsylvania, Philadelphia, PA, 19104, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Advanced Photon Source, Argonne National Laboratory, Lemont, IL, 60439, USA
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Computational Research Division, Lawrence Berkeley Laboratory, Berkeley, CA, 94720, USA
| | - Mingda Li
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA
- Quantum Measurement Group, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- IKERBASQUE, Basque Foundation for Science, Plaza Euskadi 5, Bilbao, 48009, Spain
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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13
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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14
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van Mastrigt R, Dijkstra M, van Hecke M, Coulais C. Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials. PHYSICAL REVIEW LETTERS 2022; 129:198003. [PMID: 36399748 DOI: 10.1103/physrevlett.129.198003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.
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Affiliation(s)
- Ryan van Mastrigt
- Institute of Physics, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
| | - Marjolein Dijkstra
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Department of Physics, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Martin van Hecke
- AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
- Huygens-Kamerling Onnes Lab, Universiteit Leiden, Postbus 9504, 2300 RA Leiden, The Netherlands
| | - Corentin Coulais
- Institute of Physics, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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15
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Zhao E, Mak TH, He C, Ren Z, Pak KK, Liu YJ, Jo GB. Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms. OPTICS EXPRESS 2022; 30:37786-37794. [PMID: 36258360 DOI: 10.1364/oe.473770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning algorithms enable physicists to analyze experimental data with unprecedented high sensitivities, and identify quantum phases even in the presence of unavoidable noises. Here, we report a successful identification of topological phase transitions using a deep convolutional neural network trained with low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions. We apply the trained network to unseen data to map out a whole phase diagram, which predicts the positions of the two topological phase transitions that are consistent with the results obtained by using the conventional method on higher SNR data. By visualizing the filters and post-convolutional results of the convolutional layer, we further find that the CNN uses the same information to make the classification in the system as the conventional analysis, namely spin imbalance, but with an advantage concerning SNR. Our work highlights the potential of machine learning techniques to be used in various quantum systems.
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16
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Fu Z, Liu W, Huang C, Mei T. A Review of Performance Prediction Based on Machine Learning in Materials Science. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:nano12172957. [PMID: 36079994 PMCID: PMC9457802 DOI: 10.3390/nano12172957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/07/2022] [Accepted: 08/24/2022] [Indexed: 05/11/2023]
Abstract
With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.
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Affiliation(s)
- Ziyang Fu
- School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
- Hubei Software Engineering Technology Research Center, Wuhan 430062, China
- Hubei Engineering Research Center for Smart Government and Artificial Intelligence Application, Wuhan 430062, China
| | - Weiyi Liu
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
| | - Chen Huang
- School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China
- Hubei Software Engineering Technology Research Center, Wuhan 430062, China
- Hubei Engineering Research Center for Smart Government and Artificial Intelligence Application, Wuhan 430062, China
- Correspondence: (C.H.); (T.M.)
| | - Tao Mei
- School of Materials Science and Engineering, Hubei University, Wuhan 430062, China
- Hubei Collaborative Innovation Center for Advanced Organic Chemical Materials, Wuhan 430062, China
- Key Laboratory for the Green Preparation and Application of Functional Materials, Wuhan 430062, China
- Correspondence: (C.H.); (T.M.)
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17
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Zhang H, Jiang S, Wang X, Zhang W, Huang X, Ouyang X, Yu Y, Liu Y, Deng DL, Duan LM. Experimental demonstration of adversarial examples in learning topological phases. Nat Commun 2022; 13:4993. [PMID: 36008401 PMCID: PMC9411630 DOI: 10.1038/s41467-022-32611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 08/04/2022] [Indexed: 11/08/2022] Open
Abstract
Classification and identification of different phases and the transitions between them is a central task in condensed matter physics. Machine learning, which has achieved dramatic success in a wide range of applications, holds the promise to bring unprecedented perspectives for this challenging task. However, despite the exciting progress made along this direction, the reliability of machine-learning approaches in experimental settings demands further investigation. Here, with the nitrogen-vacancy center platform, we report a proof-of-principle experimental demonstration of adversarial examples in learning topological phases. We show that the experimental noises are more likely to act as adversarial perturbations when a larger percentage of the input data are dropped or unavailable for the neural network-based classifiers. We experimentally implement adversarial examples which can deceive the phase classifier with a high confidence, while keeping the topological properties of the simulated Hopf insulators unchanged. Our results explicitly showcase the crucial vulnerability aspect of applying machine learning techniques in experiments to classify phases of matter, which can benefit future studies in this interdisciplinary field.
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Affiliation(s)
- Huili Zhang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Si Jiang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Xin Wang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Wengang Zhang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Xianzhi Huang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
- School of JiaYang, Zhejiang Shuren University, Hangzhou, 310015, P. R. China
| | - Xiaolong Ouyang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Yefei Yu
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Yanqing Liu
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China
| | - Dong-Ling Deng
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China.
- Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No. 701 Yunjin Road, Xuhui District, Shanghai, 200232, China.
| | - L-M Duan
- Center for Quantum Information, IIIS, Tsinghua University, Beijing, 100084, P. R. China.
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18
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chem Rev 2022; 122:13478-13515. [PMID: 35862246 DOI: 10.1021/acs.chemrev.2c00061] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - Dehong Chen
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
| | - David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.,Biochemistry and Chemistry, La Trobe University, Kingsbury Drive, Bundoora, Victoria 3042, Australia.,School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Rachel A Caruso
- Applied Chemistry and Environmental Science, School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia
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19
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Venderley J, Mallayya K, Matty M, Krogstad M, Ruff J, Pleiss G, Kishore V, Mandrus D, Phelan D, Poudel L, Wilson AG, Weinberger K, Upreti P, Norman M, Rosenkranz S, Osborn R, Kim EA. Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction. Proc Natl Acad Sci U S A 2022; 119:e2109665119. [PMID: 35679347 PMCID: PMC9214512 DOI: 10.1073/pnas.2109665119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/26/2022] [Indexed: 11/18/2022] Open
Abstract
The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (CaxSr[Formula: see text])3Rh4Sn13, where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd2Re2O7, to investigate its two much-debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC-revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of [Formula: see text] Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.
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Affiliation(s)
| | | | - Michael Matty
- Department of Physics, Cornell University, Ithaca, NY 14853
| | - Matthew Krogstad
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
| | - Jacob Ruff
- Cornell High Energy Synchrotron Source, Cornell University, Ithaca, NY 14853
| | - Geoff Pleiss
- Department of Computer Science, Cornell University, Ithaca, NY 14853
| | - Varsha Kishore
- Department of Computer Science, Cornell University, Ithaca, NY 14853
| | - David Mandrus
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996
| | - Daniel Phelan
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
| | - Lekhanath Poudel
- Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742
- Center for Neutron Research, National Institute of Standard and Technology, Gaithersburg, MD 20899
| | - Andrew Gordon Wilson
- Courant Institute of Mathematical Sciences, New York University, New York, NY 10012
| | - Kilian Weinberger
- Department of Computer Science, Cornell University, Ithaca, NY 14853
| | - Puspa Upreti
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
- Department of Physics, Northern Illinois University, DeKalb, IL 60115
| | - Michael Norman
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
| | - Stephan Rosenkranz
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
| | - Raymond Osborn
- Materials Science Division, Argonne National Laboratory, Lemont, IL 60439
| | - Eun-Ah Kim
- Department of Physics, Cornell University, Ithaca, NY 14853
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20
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Boeri L, Hennig R, Hirschfeld P, Profeta G, Sanna A, Zurek E, Pickett WE, Amsler M, Dias R, Eremets MI, Heil C, Hemley RJ, Liu H, Ma Y, Pierleoni C, Kolmogorov AN, Rybin N, Novoselov D, Anisimov V, Oganov AR, Pickard CJ, Bi T, Arita R, Errea I, Pellegrini C, Requist R, Gross EKU, Margine ER, Xie SR, Quan Y, Hire A, Fanfarillo L, Stewart GR, Hamlin JJ, Stanev V, Gonnelli RS, Piatti E, Romanin D, Daghero D, Valenti R. The 2021 room-temperature superconductivity roadmap. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 34:183002. [PMID: 34544070 DOI: 10.1088/1361-648x/ac2864] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 09/20/2021] [Indexed: 06/13/2023]
Abstract
Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms.In memoriam, to Neil Ashcroft, who inspired us all.
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Affiliation(s)
- Lilia Boeri
- Physics Department, Sapienza University and Enrico Fermi Research Center, Rome, Italy
| | - Richard Hennig
- Deparment of Material Science and Engineering and Quantum Theory Project, University of Florida, Gainesville 32611, United States of America
| | - Peter Hirschfeld
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | | | - Antonio Sanna
- Max Planck Institute of Microstructure Physics, Halle, Germany
| | - Eva Zurek
- University at Buffalo, SUNY, United States of America
| | | | - Maximilian Amsler
- Department of Chemistry and Biochemistry, University of Bern, Freiestrasse 3, CH-3012 Bern, Switzerland
- Department of Materials Science and Engineering, Cornell University, Ithaca, NY 14853, United States of America
| | - Ranga Dias
- University of Rochester, United States of America
| | | | | | | | - Hanyu Liu
- Jilin University, People's Republic of China
| | - Yanming Ma
- Jilin University, People's Republic of China
| | - Carlo Pierleoni
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | | | | | | | | | | | | | - Tiange Bi
- University at Buffalo, SUNY, United States of America
| | | | - Ion Errea
- University of the Basque Country, Spain
| | | | - Ryan Requist
- Max Planck Institute of Microstructure Physics, Halle, Germany
- Hebrew University of Jerusalem, Israel
| | - E K U Gross
- Max Planck Institute of Microstructure Physics, Halle, Germany
- Hebrew University of Jerusalem, Israel
| | | | - Stephen R Xie
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Yundi Quan
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Ajinkya Hire
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - Laura Fanfarillo
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - G R Stewart
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
| | - J J Hamlin
- Department of Physics, University of Florida, Gainesville, FL 32611, United States of America
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21
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Glavin NR, Ajayan PM, Kar S. Quantum Materials Manufacturing. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022:e2109892. [PMID: 35195312 DOI: 10.1002/adma.202109892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/13/2022] [Indexed: 06/14/2023]
Abstract
The quantum age is just around the corner. As quantum systems become more stable, robust, and mainstream, tackling the challenge of high-throughput manufacturing will require further developments in materials synthesis, characterization, assembly, and diagnostics. As the building blocks of future technologies scale down to atomic and molecular scales, a paradigm shift in manufacturing will begin to take shape. Inspired by a quantum manufacturing world that elevates the Materials Genome Initiative to the next level, a "human-in-the-loop" framework for high-throughput manufacturing, which addresses key opportunities and challenges to be overcome, is outlined.
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Affiliation(s)
- Nicholas R Glavin
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, OH, 45433, USA
| | - Pulickel M Ajayan
- Materials Science and Nano Engineering, Rice University, Houston, TX, 77005, USA
| | - Swastik Kar
- Department of Physics, Northeastern University, Boston, MA, 02115, USA
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22
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Xu S, McLeod AS, Chen X, Rizzo DJ, Jessen BS, Yao Z, Wang Z, Sun Z, Shabani S, Pasupathy AN, Millis AJ, Dean CR, Hone JC, Liu M, Basov DN. Deep Learning Analysis of Polaritonic Wave Images. ACS NANO 2021; 15:18182-18191. [PMID: 34714043 DOI: 10.1021/acsnano.1c07011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Deep learning (DL) is an emerging analysis tool across the sciences and engineering. Encouraged by the successes of DL in revealing quantitative trends in massive imaging data, we applied this approach to nanoscale deeply subdiffractional images of propagating polaritonic waves in complex materials. Utilizing the convolutional neural network (CNN), we developed a practical protocol for the rapid regression of images that quantifies the wavelength and the quality factor of polaritonic waves. Using simulated near-field images as training data, the CNN can be made to simultaneously extract polaritonic characteristics and material parameters in a time scale that is at least 3 orders of magnitude faster than common fitting/processing procedures. The CNN-based analysis was validated by examining the experimental near-field images of charge-transfer plasmon polaritons at graphene/α-RuCl3 interfaces. Our work provides a general framework for extracting quantitative information from images generated with a variety of scanning probe methods.
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Affiliation(s)
- Suheng Xu
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Alexander S McLeod
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Xinzhong Chen
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, 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
- Department of Mechanical Engineering, Columbia University, New York, New York 10027, United States
| | - Ziheng Yao
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Zhicai Wang
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
| | - Zhiyuan Sun
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - Sara Shabani
- 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
| | - Andrew J Millis
- Department of Physics, Columbia University, New York, New York 10027, United States
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, United States
| | - Cory R Dean
- Department of Physics, Columbia University, New York, New York 10027, United States
| | - James C Hone
- Department of Mechanical Engineering, 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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23
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Bohrdt A, Kim S, Lukin A, Rispoli M, Schittko R, Knap M, Greiner M, Léonard J. Analyzing Nonequilibrium Quantum States through Snapshots with Artificial Neural Networks. PHYSICAL REVIEW LETTERS 2021; 127:150504. [PMID: 34678012 DOI: 10.1103/physrevlett.127.150504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 08/11/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Current quantum simulation experiments are starting to explore nonequilibrium many-body dynamics in previously inaccessible regimes in terms of system sizes and timescales. Therefore, the question emerges as to which observables are best suited to study the dynamics in such quantum many-body systems. Using machine learning techniques, we investigate the dynamics and, in particular, the thermalization behavior of an interacting quantum system that undergoes a nonequilibrium phase transition from an ergodic to a many-body localized phase. We employ supervised and unsupervised training methods to distinguish nonequilibrium from equilibrium data, using the network performance as a probe for the thermalization behavior of the system. We test our methods with experimental snapshots of ultracold atoms taken with a quantum gas microscope. Our results provide a path to analyze highly entangled large-scale quantum states for system sizes where numerical calculations of conventional observables become challenging.
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Affiliation(s)
- A Bohrdt
- Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), Schellingstrasse 4, D-80799 München, Germany
- ITAMP, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts 02138, USA
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - S Kim
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - A Lukin
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - M Rispoli
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - R Schittko
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - M Knap
- Department of Physics and Institute for Advanced Study, Technical University of Munich, 85748 Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), Schellingstrasse 4, D-80799 München, Germany
| | - M Greiner
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
| | - J Léonard
- Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA
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24
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Diegmiller R, Doherty CA, Stern T, Imran Alsous J, Shvartsman SY. Size scaling in collective cell growth. Development 2021; 148:271938. [PMID: 34463760 DOI: 10.1242/dev.199663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 08/12/2021] [Indexed: 02/03/2023]
Abstract
Size is a fundamental feature of living entities and is intimately tied to their function. Scaling laws, which can be traced to D'Arcy Thompson and Julian Huxley, have emerged as a powerful tool for studying regulation of the growth dynamics of organisms and their constituent parts. Yet, throughout the 20th century, as scaling laws were established for single cells, quantitative studies of the coordinated growth of multicellular structures have lagged, largely owing to technical challenges associated with imaging and image processing. Here, we present a supervised learning approach for quantifying the growth dynamics of germline cysts during oogenesis. Our analysis uncovers growth patterns induced by the groupwise developmental dynamics among connected cells, and differential growth rates of their organelles. We also identify inter-organelle volumetric scaling laws, finding that nurse cell growth is linear over several orders of magnitude. Our approach leverages the ever-increasing quantity and quality of imaging data, and is readily amenable for studies of collective cell growth in other developmental contexts, including early mammalian embryogenesis and germline development.
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Affiliation(s)
- Rocky Diegmiller
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Caroline A Doherty
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.,Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Tomer Stern
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.,Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA
| | - Jasmin Imran Alsous
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.,Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Stanislav Y Shvartsman
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.,Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.,Flatiron Institute, Simons Foundation, New York, NY 10010, USA
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25
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Rao N, Liu K, Pollet L. Inferring hidden symmetries of exotic magnets from detecting explicit order parameters. Phys Rev E 2021; 104:015311. [PMID: 34412223 DOI: 10.1103/physreve.104.015311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 06/13/2021] [Indexed: 11/07/2022]
Abstract
An unconventional magnet may be mapped onto a simple ferromagnet by the existence of a high-symmetry point. Knowledge of conventional ferromagnetic systems may then be carried over to provide insight into more complex orders. Here we demonstrate how an unsupervised and interpretable machine-learning approach can be used to search for potential high-symmetry points in unconventional magnets without any prior knowledge of the system. The method is applied to the classical Heisenberg-Kitaev model on a honeycomb lattice, where our machine learns the transformations that manifest its hidden O(3) symmetry, without using data of these high-symmetry points. Moreover, we clarify that, in contrast to the stripy and zigzag orders, a set of D_{2} and D_{2h} ordering matrices provides a more complete description of the magnetization in the Heisenberg-Kitaev model. In addition, our machine also learns the local constraints at the phase boundaries, which manifest a subdimensional symmetry. This paper highlights the importance of explicit order parameters to many-body spin systems and the property of interpretability for the physical application of machine-learning techniques.
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Affiliation(s)
- Nihal Rao
- Arnold Sommerfeld Center for Theoretical Physics, University of Munich, Theresienstrasse 37, 80333 München, Germany.,Munich Center for Quantum Science and Technology, Schellingstrasse 4, 80799 München, Germany
| | - Ke Liu
- Arnold Sommerfeld Center for Theoretical Physics, University of Munich, Theresienstrasse 37, 80333 München, Germany.,Munich Center for Quantum Science and Technology, Schellingstrasse 4, 80799 München, Germany
| | - Lode Pollet
- Arnold Sommerfeld Center for Theoretical Physics, University of Munich, Theresienstrasse 37, 80333 München, Germany.,Munich Center for Quantum Science and Technology, Schellingstrasse 4, 80799 München, Germany.,Wilczek Quantum Center, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
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26
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Käming N, Dawid A, Kottmann K, Lewenstein M, Sengstock K, Dauphin A, Weitenberg C. Unsupervised machine learning of topological phase transitions from experimental data. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abffe7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Abstract
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries from noisy and imperfect data without the knowledge of the order parameter. Here, we apply different unsupervised machine learning techniques, including anomaly detection and influence functions, to experimental data from ultracold atoms. In this way, we obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. We show that these methods can successfully be applied to experimental data at finite temperatures and to the data of Floquet systems when post-processing the data to a single micromotion phase. Our work provides a benchmark for the unsupervised detection of new exotic phases in complex many-body systems.
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27
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Miles C, Bohrdt A, Wu R, Chiu C, Xu M, Ji G, Greiner M, Weinberger KQ, Demler E, Kim EA. Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data. Nat Commun 2021; 12:3905. [PMID: 34162847 PMCID: PMC8222395 DOI: 10.1038/s41467-021-23952-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 05/27/2021] [Indexed: 11/09/2022] Open
Abstract
Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.
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Affiliation(s)
- Cole Miles
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Annabelle Bohrdt
- Department of Physics, Harvard University, Cambridge, MA, USA
- Department of Physics and Institute for Advanced Study, Technical University of Munich, Garching, Germany
- Munich Center for Quantum Science and Technology (MCQST), München, Germany
| | - Ruihan Wu
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Christie Chiu
- Department of Physics, Harvard University, Cambridge, MA, USA
- Department of Electrical Engineering, Princeton University, Princeton, NJ, USA
- Princeton Center for Complex Materials, Princeton University, Princeton, NJ, USA
| | - Muqing Xu
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Geoffrey Ji
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Markus Greiner
- Department of Physics, Harvard University, Cambridge, MA, USA
| | | | - Eugene Demler
- Department of Physics, Harvard University, Cambridge, MA, USA
| | - Eun-Ah Kim
- Department of Physics, Cornell University, Ithaca, NY, USA.
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28
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Twyman R, Gibson SJ, Molony J, Quintanilla J. Principal component analysis of diffuse magnetic neutron scattering: a theoretical study. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 33:324002. [PMID: 34038888 DOI: 10.1088/1361-648x/ac056f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 05/26/2021] [Indexed: 06/12/2023]
Abstract
We present a theoretical study of the potential of principal component (PC) analysis to analyse magnetic diffuse neutron scattering data on quantum materials. To address this question, we simulate the scattering functionSqfor a model describing a cluster magnet with anisotropic spin-spin interactions under different conditions of applied field and temperature. We find high dimensionality reduction and that the algorithm can be trained with surprisingly small numbers of simulated observations. Subsequently, observations can be projected onto the reduced-dimensionality space defined by the learnt PCs. Constant-field temperature scans correspond to trajectories in this space which show characteristic bifurcations at the critical fields corresponding to ground-state phase boundaries. Such plots allow the ground-state phase diagram to be accurately determined from finite-temperature measurements.
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Affiliation(s)
- Robert Twyman
- School of Physical Sciences, University of Kent, Canterbury, Kent, CT2 7NH, United Kingdom
| | - Stuart J Gibson
- School of Physical Sciences, University of Kent, Canterbury, Kent, CT2 7NH, United Kingdom
| | - James Molony
- Department of Physics, Durham University, Lower Mountjoy, South Road, Durham DH1 3LE, United Kingdom
| | - Jorge Quintanilla
- School of Physical Sciences, University of Kent, Canterbury, Kent, CT2 7NH, United Kingdom
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29
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Vinograd I, Zhou R, Hirata M, Wu T, Mayaffre H, Krämer S, Liang R, Hardy WN, Bonn DA, Julien MH. Locally commensurate charge-density wave with three-unit-cell periodicity in YBa 2Cu 3O y. Nat Commun 2021; 12:3274. [PMID: 34075033 PMCID: PMC8169916 DOI: 10.1038/s41467-021-23140-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/16/2021] [Indexed: 11/20/2022] Open
Abstract
In order to identify the mechanism responsible for the formation of charge-density waves (CDW) in cuprate superconductors, it is important to understand which aspects of the CDW's microscopic structure are generic and which are material-dependent. Here, we show that, at the local scale probed by NMR, long-range CDW order in YBa2Cu3Oy is unidirectional with a commensurate period of three unit cells (λ = 3b), implying that the incommensurability found in X-ray scattering is ensured by phase slips (discommensurations). Furthermore, NMR spectra reveal a predominant oxygen character of the CDW with an out-of-phase relationship between certain lattice sites but no specific signature of a secondary CDW with λ = 6b associated with a putative pair-density wave. These results shed light on universal aspects of the cuprate CDW. In particular, its spatial profile appears to generically result from the interplay between an incommensurate tendency at long length scales, possibly related to properties of the Fermi surface, and local commensuration effects, due to electron-electron interactions or lock-in to the lattice.
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Affiliation(s)
- Igor Vinograd
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France.
| | - Rui Zhou
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France
- Institute of Physics, Chinese Academy of Sciences, and Beijing National Laboratory for Condensed Matter Physics, Beijing, China
| | - Michihiro Hirata
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France
- MPA-Q, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Tao Wu
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui, China
| | - Hadrien Mayaffre
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France
| | - Steffen Krämer
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France
| | - Ruixing Liang
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Canadian Institute for Advanced Research, Toronto, Canada
| | - W N Hardy
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Canadian Institute for Advanced Research, Toronto, Canada
| | - D A Bonn
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Canadian Institute for Advanced Research, Toronto, Canada
| | - Marc-Henri Julien
- Univ. Grenoble Alpes, INSA Toulouse, Univ. Toulouse Paul Sabatier, EMFL, CNRS, LNCMI, Grenoble, France.
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30
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Sotres J, Boyd H, Gonzalez-Martinez JF. Enabling autonomous scanning probe microscopy imaging of single molecules with deep learning. NANOSCALE 2021; 13:9193-9203. [PMID: 33885692 DOI: 10.1039/d1nr01109j] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Scanning probe microscopies allow investigating surfaces at the nanoscale, in real space and with unparalleled signal-to-noise ratio. However, these microscopies are not used as much as it would be expected considering their potential. The main limitations preventing a broader use are the need of experienced users, the difficulty in data analysis and the time-consuming nature of experiments that require continuous user supervision. In this work, we addressed the latter and developed an algorithm that controlled the operation of an Atomic Force Microscope (AFM) that, without the need of user intervention, allowed acquiring multiple high-resolution images of different molecules. We used DNA on mica as a model sample to test our control algorithm, which made use of two deep learning techniques that so far have not been used for real time SPM automation. One was an object detector, YOLOv3, which provided the location of molecules in the captured images. The second was a Siamese network that could identify the same molecule in different images. This allowed both performing a series of images on selected molecules while incrementing the resolution, as well as keeping track of molecules already imaged at high resolution, avoiding loops where the same molecule would be imaged an unlimited number of times. Overall, our implementation of deep learning techniques brings SPM a step closer to full autonomous operation.
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Affiliation(s)
- Javier Sotres
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden and Biofilms-Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden.
| | - Hannah Boyd
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden and Biofilms-Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden.
| | - Juan F Gonzalez-Martinez
- Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden and Biofilms-Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden.
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31
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Tula T, Möller G, Quintanilla J, Giblin SR, Hillier AD, McCabe EE, Ramos S, Barker DS, Gibson S. Machine learning approach to muon spectroscopy analysis. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 33:194002. [PMID: 33545697 DOI: 10.1088/1361-648x/abe39e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 02/05/2021] [Indexed: 06/12/2023]
Abstract
In recent years, artificial intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised machine learning (ML) algorithm called principal component analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase transitions in various materials. The measured quantity in muon spectroscopy is an asymmetry function, which may hold information about the distribution of the intrinsic magnetic field in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions-measured at different temperatures-might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right fitting function requires knowledge about the underlying physics of the probed material. Conversely, PCA focuses on small differences in the asymmetry curves and works without any prior assumptions about the studied samples. We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known. Additionally, we found out that our ML technique seems to work best with large numbers of measurements, regardless of whether the algorithm takes data only for a single material or whether the analysis is performed simultaneously for many materials with different physical properties.
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Affiliation(s)
- T Tula
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
| | - G Möller
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
| | - J Quintanilla
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
| | - S R Giblin
- School of Physics and Astronomy, Cardiff University, Cardiff CF24 3AA, United Kingdom
| | - A D Hillier
- ISIS Facility, STFC Rutherford Appleton Laboratory, Chilton, Didcot Oxon, OX11 0QX, United Kingdom
| | - E E McCabe
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
| | - S Ramos
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
| | - D S Barker
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
- School of Physics and Astronomy, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - S Gibson
- School of Physical Sciences, University of Kent, Park Wood Rd, Canterbury CT2 7NH, United Kingdom
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32
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Rodrigues JD, Dhar HS, Walker BT, Smith JM, Oulton RF, Mintert F, Nyman RA. Learning the Fuzzy Phases of Small Photonic Condensates. PHYSICAL REVIEW LETTERS 2021; 126:150602. [PMID: 33929251 DOI: 10.1103/physrevlett.126.150602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
Phase transitions, being the ultimate manifestation of collective behavior, are typically features of many-particle systems only. Here, we describe the experimental observation of collective behavior in small photonic condensates made up of only a few photons. Moreover, a wide range of both equilibrium and nonequilibrium regimes, including Bose-Einstein condensation or laserlike emission are identified. However, the small photon number and the presence of large relative fluctuations places major difficulties in identifying different phases and phase transitions. We overcome this limitation by employing unsupervised learning and fuzzy clustering algorithms to systematically construct the fuzzy phase diagram of our small photonic condensate. Our results thus demonstrate the rich and complex phase structure of even small collections of photons, making them an ideal platform to investigate equilibrium and nonequilibrium physics at the few particle level.
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Affiliation(s)
- João D Rodrigues
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
| | - Himadri S Dhar
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
| | - Benjamin T Walker
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
- Centre for Doctoral Training in Controlled Quantum Dynamics, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
| | - Jason M Smith
- Department of Materials, University of Oxford, Oxford OX2 6NN, United Kingdom
| | - Rupert F Oulton
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
| | - Florian Mintert
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
| | - Robert A Nyman
- Physics Department, Blackett Laboratory, Imperial College London, Prince Consort Road, SW7 2AZ, United Kingdom
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33
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Zhao E, Lee J, He C, Ren Z, Hajiyev E, Liu J, Jo GB. Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks. Nat Commun 2021; 12:2011. [PMID: 33790292 PMCID: PMC8012572 DOI: 10.1038/s41467-021-22270-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 03/02/2021] [Indexed: 11/10/2022] Open
Abstract
The power of machine learning (ML) provides the possibility of analyzing experimental measurements with a high sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU(N) spin symmetry prepared in a quantum simulator. Although such spin symmetry should manifest itself in a many-body wavefunction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of ~94% for detection of the spin multiplicity, we investigate how the accuracy depends on various less-pronounced effects with filtered experimental images. Guided by our machinery, we directly measure a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU(N) Fermi liquids, and to identify less-pronounced effects even for highly complex quantum matter with minimal prior understanding.
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Grants
- 26302118 Research Grants Council, University Grants Committee (RGC, UGC)
- 16305019 Research Grants Council, University Grants Committee (RGC, UGC)
- N-HKUST626/18 Research Grants Council, University Grants Committee (RGC, UGC)
- 16311516 Research Grants Council, University Grants Committee (RGC, UGC)
- 16305317 Research Grants Council, University Grants Committee (RGC, UGC)
- 16304918 Research Grants Council, University Grants Committee (RGC, UGC)
- 16306119 Research Grants Council, University Grants Committee (RGC, UGC)
- C6005-17G Research Grants Council, University Grants Committee (RGC, UGC)
- N-HKUST601/17 Research Grants Council, University Grants Committee (RGC, UGC)
- Innovation Awards Croucher Foundation
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Affiliation(s)
- Entong Zhao
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Jeongwon Lee
- HKUST Jockey Club Institute of Advanced Study, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Chengdong He
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Zejian Ren
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Elnur Hajiyev
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Junwei Liu
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
| | - Gyu-Boong Jo
- Department of Physics, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China.
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34
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Roest LI, van Heijst SE, Maduro L, Rojo J, Conesa-Boj S. Charting the low-loss region in electron energy loss spectroscopy with machine learning. Ultramicroscopy 2021; 222:113202. [PMID: 33453606 DOI: 10.1016/j.ultramic.2021.113202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/22/2020] [Accepted: 01/05/2021] [Indexed: 11/25/2022]
Abstract
Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS2 nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS2, finding EBG=1.6-0.2+0.3eV with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter.
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Affiliation(s)
- Laurien I Roest
- Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands; Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands
| | - Sabrya E van Heijst
- Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands
| | - Louis Maduro
- Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands
| | - Juan Rojo
- Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands; Department of Physics and Astronomy, VU, 1081 HV Amsterdam, The Netherlands
| | - Sonia Conesa-Boj
- Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands.
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35
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Lidiak A, Gong Z. Unsupervised Machine Learning of Quantum Phase Transitions Using Diffusion Maps. PHYSICAL REVIEW LETTERS 2020; 125:225701. [PMID: 33315426 DOI: 10.1103/physrevlett.125.225701] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 10/15/2020] [Indexed: 06/12/2023]
Abstract
Experimental quantum simulators have become large and complex enough that discovering new physics from the huge amount of measurement data can be quite challenging, especially when little theoretical understanding of the simulated model is available. Unsupervised machine learning methods are particularly promising in overcoming this challenge. For the specific task of learning quantum phase transitions, unsupervised machine learning methods have primarily been developed for phase transitions characterized by simple order parameters, typically linear in the measured observables. However, such methods often fail for more complicated phase transitions, such as those involving incommensurate phases, valence-bond solids, topological order, and many-body localization. We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning such complex phase transitions unsupervised. This method may work for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators as a versatile tool for learning various quantum phases and phase transitions.
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Affiliation(s)
- Alexander Lidiak
- Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA
| | - Zhexuan Gong
- Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA
- National Institute of Standards and Technology, Boulder, Colorado 80305, USA
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36
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Zhang YH, Zheng PL, Zhang Y, Deng DL. Topological Quantum Compiling with Reinforcement Learning. PHYSICAL REVIEW LETTERS 2020; 125:170501. [PMID: 33156669 DOI: 10.1103/physrevlett.125.170501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 09/08/2020] [Accepted: 09/14/2020] [Indexed: 06/11/2023]
Abstract
Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
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Affiliation(s)
- Yuan-Hang Zhang
- Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, People's Republic of China
- Department of Physics, University of California, San Diego, California 92093, USA
| | - Pei-Lin Zheng
- International Center for Quantum Materials, Peking University, Beijing 100871, China
- School of Physics, Peking University, Beijing 100871, China
| | - Yi Zhang
- International Center for Quantum Materials, Peking University, Beijing 100871, China
- School of Physics, Peking University, Beijing 100871, China
| | - Dong-Ling Deng
- Center for Quantum Information, IIIS, Tsinghua University, Beijing 100084, People's Republic of China
- Shanghai Qi Zhi Institute, 41th Floor, AI Tower, No. 701 Yunjin Road, Xuhui District, Shanghai 200232, China
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37
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Gordon OM, Hodgkinson JEA, Farley SM, Hunsicker EL, Moriarty PJ. Automated Searching and Identification of Self-Organized Nanostructures. NANO LETTERS 2020; 20:7688-7693. [PMID: 32866019 DOI: 10.1021/acs.nanolett.0c03213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Currently, researchers spend significant time manually searching through large volumes of data produced during scanning probe imaging to identify specific patterns and motifs formed via self-assembly and self-organization. Here, we use a combination of Monte Carlo simulations, general statistics, and machine learning to automatically distinguish several spatially correlated patterns in a mixed, highly varied data set of real AFM images of self-organized nanoparticles. We do this regardless of feature-scale and without the need for manually labeled training data. Provided that the structures of interest can be simulated, the strategy and protocols we describe can be easily adapted to other self-organized systems and data sets.
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Affiliation(s)
- Oliver M Gordon
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Jo E A Hodgkinson
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
| | - Steff M Farley
- School of Science, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Eugénie L Hunsicker
- School of Science, Loughborough University, Loughborough LE11 3TU, United Kingdom
| | - Philip J Moriarty
- School of Physics and Astronomy, University of Nottingham, Nottingham NG7 2RD, United Kingdom
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38
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Solving optimization tasks in condensed matter. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-00240-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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39
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Wang D, Wei S, Yuan A, Tian F, Cao K, Zhao Q, Zhang Y, Zhou C, Song X, Xue D, Yang S. Machine Learning Magnetic Parameters from Spin Configurations. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2020; 7:2000566. [PMID: 32832350 PMCID: PMC7435232 DOI: 10.1002/advs.202000566] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 05/29/2020] [Indexed: 05/27/2023]
Abstract
Hamiltonian parameters estimation is crucial in condensed matter physics, but is time- and cost-consuming. High-resolution images provide detailed information of underlying physics, but extracting Hamiltonian parameters from them is difficult due to the huge Hilbert space. Here, a protocol for Hamiltonian parameters estimation from images based on a machine learning (ML) architecture is provided. It consists in learning a mapping between spin configurations and Hamiltonian parameters from a small amount of simulated images, applying the trained ML model to a single unexplored experimental image to estimate its key parameters, and predicting the corresponding materials properties by a physical model. The efficiency of the approach is demonstrated by reproducing the same spin configuration as the experimental one and predicting the coercive field, the saturation field, and even the volume of the experiment specimen accurately. The proposed approach paves a way to achieve a stable and efficient parameters estimation.
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Affiliation(s)
- Dingchen Wang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Songrui Wei
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong ProvinceCollege of Optoelectronic EngineeringShenzhen UniversityShenzhen518060China
| | - Anran Yuan
- Key Laboratory of Intelligent Perception and Image Understanding of Ministry of EducationInternational Research Center for Intelligent Perception and ComputationJoint International Research Laboratory of Intelligent Perception and ComputationSchool of Artificial IntelligenceXidian UniversityXi'an710071China
| | - Fanghua Tian
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Kaiyan Cao
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Qizhong Zhao
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Yin Zhang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Chao Zhou
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Xiaoping Song
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Dezhen Xue
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
| | - Sen Yang
- MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed MatterSchool of ScienceState Key Laboratory for Mechanical Behavior of MaterialsXi'an Jiaotong UniversityXi'an710049China
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Bartkiewicz K, Gneiting C, Černoch A, Jiráková K, Lemr K, Nori F. Experimental kernel-based quantum machine learning in finite feature space. Sci Rep 2020; 10:12356. [PMID: 32704032 PMCID: PMC7378258 DOI: 10.1038/s41598-020-68911-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/24/2020] [Indexed: 11/10/2022] Open
Abstract
We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels' ability to separate points, i.e., their "resolution," under the constraint of finite, fixed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classification tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature.
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Affiliation(s)
- Karol Bartkiewicz
- Faculty of Physics, Adam Mickiewicz University, 61-614, Poznan, Poland.
- RCPTM, Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17. listopadu 12, 771 46, Olomouc, Czech Republic.
| | - Clemens Gneiting
- Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, 351-0198, Japan
| | - Antonín Černoch
- RCPTM, Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17. listopadu 12, 771 46, Olomouc, Czech Republic.
| | - Kateřina Jiráková
- RCPTM, Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17. listopadu 12, 771 46, Olomouc, Czech Republic
| | - Karel Lemr
- RCPTM, Joint Laboratory of Optics of Palacký University and Institute of Physics of Czech Academy of Sciences, 17. listopadu 12, 771 46, Olomouc, Czech Republic.
| | - Franco Nori
- Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, 351-0198, Japan
- Department of Physics, The University of Michigan, Ann Arbor, MI, 48109-1040, USA
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Atomic-scale electronic structure of the cuprate pair density wave state coexisting with superconductivity. Proc Natl Acad Sci U S A 2020; 117:14805-14811. [PMID: 32546526 PMCID: PMC7334493 DOI: 10.1073/pnas.2002429117] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
By making a variety of quantitative comparisons between electronic visualization experiments and a theory describing coexisting pair density wave and superconductive states in cuprates, we find striking correspondence throughout. Our model can thus explain the microscopic origins of many key atomic-scale phenomena of the cuprate broken-symmetry state. These observations are consistent with the possibility that a short-range pair density wave (PDW) state coexists with superconductivity below a critical hole density in Bi2Sr2CaCu2O8, that the charge density wave modulations in cuprates are a consequence of the PDW state, that the cuprate pseudogap is the antinodal gap of the PDW, and that the critical point in the cuprate phase diagram occurs due to disappearance of the PDW. The defining characteristic of hole-doped cuprates is d-wave high temperature superconductivity. However, intense theoretical interest is now focused on whether a pair density wave state (PDW) could coexist with cuprate superconductivity [D. F. Agterberg et al., Annu. Rev. Condens. Matter Phys. 11, 231 (2020)]. Here, we use a strong-coupling mean-field theory of cuprates, to model the atomic-scale electronic structure of an eight-unit-cell periodic, d-symmetry form factor, pair density wave (PDW) state coexisting with d-wave superconductivity (DSC). From this PDW + DSC model, the atomically resolved density of Bogoliubov quasiparticle states Nr,E is predicted at the terminal BiO surface of Bi2Sr2CaCu2O8 and compared with high-precision electronic visualization experiments using spectroscopic imaging scanning tunneling microscopy (STM). The PDW + DSC model predictions include the intraunit-cell structure and periodic modulations of Nr,E, the modulations of the coherence peak energy Δpr, and the characteristics of Bogoliubov quasiparticle interference in scattering-wavevector space q-space. Consistency between all these predictions and the corresponding experiments indicates that lightly hole-doped Bi2Sr2CaCu2O8 does contain a PDW + DSC state. Moreover, in the model the PDW + DSC state becomes unstable to a pure DSC state at a critical hole density p*, with empirically equivalent phenomena occurring in the experiments. All these results are consistent with a picture in which the cuprate translational symmetry-breaking state is a PDW, the observed charge modulations are its consequence, the antinodal pseudogap is that of the PDW state, and the cuprate critical point at p* ≈ 19% occurs due to disappearance of this PDW.
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Gordon OM, Moriarty PJ. Machine learning at the (sub)atomic scale: next generation scanning probe microscopy. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab7d2f] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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43
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Ghosh S, Matty M, Baumbach R, Bauer ED, Modic KA, Shekhter A, Mydosh JA, Kim EA, Ramshaw BJ. One-component order parameter in URu 2Si 2 uncovered by resonant ultrasound spectroscopy and machine learning. SCIENCE ADVANCES 2020; 6:eaaz4074. [PMID: 32181367 PMCID: PMC7060057 DOI: 10.1126/sciadv.aaz4074] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 12/10/2019] [Indexed: 06/10/2023]
Abstract
The unusual correlated state that emerges in URu2Si2 below T HO = 17.5 K is known as "hidden order" because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are "hidden." We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T HO. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems.
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Affiliation(s)
- Sayak Ghosh
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA
| | - Michael Matty
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA
| | - Ryan Baumbach
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32310, USA
| | - Eric D. Bauer
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - K. A. Modic
- Max Planck Institute for Chemical Physics of Solids, Dresden 01187, Germany
| | - Arkady Shekhter
- National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32310, USA
| | - J. A. Mydosh
- Kamerlingh Onnes Laboratory and Institute Lorentz, Leiden University, 2300RA Leiden, Netherlands
| | - Eun-Ah Kim
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA
| | - B. J. Ramshaw
- Laboratory of Atomic and Solid State Physics, Cornell University, Ithaca, NY 14853, USA
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Gordon OM, Junqueira FLQ, Moriarty PJ. Embedding human heuristics in machine-learning-enabled probe microscopy. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab42ec] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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45
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Agar JC, Naul B, Pandya S, van der Walt S, Maher J, Ren Y, Chen LQ, Kalinin SV, Vasudevan RK, Cao Y, Bloom JS, Martin LW. Revealing ferroelectric switching character using deep recurrent neural networks. Nat Commun 2019; 10:4809. [PMID: 31641122 PMCID: PMC6805893 DOI: 10.1038/s41467-019-12750-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 09/18/2019] [Indexed: 11/22/2022] Open
Abstract
The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr0.2Ti0.8O3 with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material’s physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials. The scale and dimensionality of imaging data means information is commonly overlooked. Here, using recurrent neural networks we understand temporal dependencies in hyperspectral imagery, enabling the observation of differences in ferroelectric switching mechanisms in PbZr0.2Ti0.8O3 thin films due to formation of charged domain walls.
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Affiliation(s)
- Joshua C Agar
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. .,Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA.
| | - Brett Naul
- Department of Astronomy, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Shishir Pandya
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Stefan van der Walt
- Berkeley Institute of Data Science, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Joshua Maher
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Yao Ren
- Department of Materials Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA
| | - Long-Qing Chen
- Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, 16802-5006, USA
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Ye Cao
- Department of Materials Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA
| | - Joshua S Bloom
- Department of Astronomy, University of California, Berkeley, Berkeley, CA, 94720, USA
| | - Lane W Martin
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA. .,Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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Linking the pseudogap in the cuprates with local symmetry breaking: A commentary. Proc Natl Acad Sci U S A 2019; 116:14395-14397. [PMID: 31285324 PMCID: PMC6642401 DOI: 10.1073/pnas.1908786116] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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