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Kalinin SV, Ziatdinov M, Hinkle J, Jesse S, Ghosh A, Kelley KP, Lupini AR, Sumpter BG, Vasudevan RK. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. ACS NANO 2021; 15:12604-12627. [PMID: 34269558 DOI: 10.1021/acsnano.1c02104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.
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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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Melton CN, Noack MM, Ohta T, Beechem TE, Robinson J, Zhang X, Bostwick A, Jozwiak C, Koch RJ, Zwart PH, Hexemer A, Rotenberg E. K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/abab61] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.
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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: 21] [Impact Index Per Article: 5.3] [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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Ito K, Ogawa Y, Yokota K, Matsumura S, Minamisawa T, Suga K, Shiba K, Kimura Y, Hirano-Iwata A, Takamura Y, Ogino T. Host Cell Prediction of Exosomes Using Morphological Features on Solid Surfaces Analyzed by Machine Learning. J Phys Chem B 2018; 122:6224-6235. [PMID: 29771528 DOI: 10.1021/acs.jpcb.8b01646] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Exosomes are extracellular nanovesicles released from any cells and found in any body fluid. Because exosomes exhibit information of their host cells (secreting cells), their analysis is expected to be a powerful tool for early diagnosis of cancers. To predict the host cells, we extracted multidimensional feature data about size, shape, and deformation of exosomes immobilized on solid surfaces by atomic force microscopy (AFM). The key idea is combination of support vector machine (SVM) learning for individual exosome particles and their interpretation by principal component analysis (PCA). We observed exosomes derived from three different cancer cells on SiO2/Si, 3-aminopropyltriethoxysilane-modified-SiO2/Si, and TiO2 substrates by AFM. Then, 14-dimensional feature vectors were extracted from AFM particle data, and classifiers were trained in 14-dimensional space. The prediction accuracy for host cells of test AFM particles was examined by the cross-validation test. As a result, we obtained prediction of exosome host cells with the best accuracy of 85.2% for two-class SVM learning and 82.6% for three-class one. By PCA of the particle classifiers, we concluded that the main factors for prediction accuracy and its strong dependence on substrates are incremental decrease in the PCA-defined aspect ratio of the particles with their volume.
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Affiliation(s)
- Kazuki Ito
- Yokohama National University , 79-5, Tokiwadai , Hodogaya-ku, Yokohama 240-8501 , Japan
| | - Yuta Ogawa
- Yokohama National University , 79-5, Tokiwadai , Hodogaya-ku, Yokohama 240-8501 , Japan
| | - Keiji Yokota
- Yokohama National University , 79-5, Tokiwadai , Hodogaya-ku, Yokohama 240-8501 , Japan
| | - Sachiko Matsumura
- Japanese Foundation for Cancer Research , 3-8-31 Ariake , Koto-ku, Tokyo 135-8550 , Japan
| | - Tamiko Minamisawa
- Japanese Foundation for Cancer Research , 3-8-31 Ariake , Koto-ku, Tokyo 135-8550 , Japan
| | - Kanako Suga
- Japanese Foundation for Cancer Research , 3-8-31 Ariake , Koto-ku, Tokyo 135-8550 , Japan
| | - Kiyotaka Shiba
- Japanese Foundation for Cancer Research , 3-8-31 Ariake , Koto-ku, Tokyo 135-8550 , Japan
| | - Yasuo Kimura
- Tokyo University of Technology , 1404-1, Katakura-Cho , Hachioji 192-0914 , Japan
| | - Ayumi Hirano-Iwata
- Tohoku University , 2-1-1, Katahira , Aoba-ku, Sendai , Miyagi 980-8577 , Japan
| | - Yuzuru Takamura
- Japan Advanced Institute of Science and Technology , 1-1, Asahi-Dai , Nomi , Ishikawa 923-1292 , Japan
| | - Toshio Ogino
- Yokohama National University , 79-5, Tokiwadai , Hodogaya-ku, Yokohama 240-8501 , Japan.,Japan Advanced Institute of Science and Technology , 1-1, Asahi-Dai , Nomi , Ishikawa 923-1292 , Japan
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Kannan R, Ievlev AV, Laanait N, Ziatdinov MA, Vasudevan RK, Jesse S, Kalinin SV. Deep data analysis via physically constrained linear unmixing: universal framework, domain examples, and a community-wide platform. ADVANCED STRUCTURAL AND CHEMICAL IMAGING 2018; 4:6. [PMID: 29755927 PMCID: PMC5928180 DOI: 10.1186/s40679-018-0055-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 03/19/2018] [Indexed: 01/05/2023]
Abstract
Many spectral responses in materials science, physics, and chemistry experiments can be characterized as resulting from the superposition of a number of more basic individual spectra. In this context, unmixing is defined as the problem of determining the individual spectra, given measurements of multiple spectra that are spatially resolved across samples, as well as the determination of the corresponding abundance maps indicating the local weighting of each individual spectrum. Matrix factorization is a popular linear unmixing technique that considers that the mixture model between the individual spectra and the spatial maps is linear. Here, we present a tutorial paper targeted at domain scientists to introduce linear unmixing techniques, to facilitate greater understanding of spectroscopic imaging data. We detail a matrix factorization framework that can incorporate different domain information through various parameters of the matrix factorization method. We demonstrate many domain-specific examples to explain the expressivity of the matrix factorization framework and show how the appropriate use of domain-specific constraints such as non-negativity and sum-to-one abundance result in physically meaningful spectral decompositions that are more readily interpretable. Our aim is not only to explain the off-the-shelf available tools, but to add additional constraints when ready-made algorithms are unavailable for the task. All examples use the scalable open source implementation from https://github.com/ramkikannan/nmflibrary that can run from small laptops to supercomputers, creating a user-wide platform for rapid dissemination and adoption across scientific disciplines.
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Affiliation(s)
- R. Kannan
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - A. V. Ievlev
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - N. Laanait
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - M. A. Ziatdinov
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - R. K. Vasudevan
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - S. Jesse
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - S. V. Kalinin
- The Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
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Ziatdinov M, Maksov A, Kalinin SV. Deep Data Analytics in Structural and Functional Imaging of Nanoscale Materials. MATERIALS DISCOVERY AND DESIGN 2018. [DOI: 10.1007/978-3-319-99465-9_5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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8
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Improving superconductivity in BaFe 2As 2-based crystals by cobalt clustering and electronic uniformity. Sci Rep 2017; 7:949. [PMID: 28424488 PMCID: PMC5430462 DOI: 10.1038/s41598-017-00984-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Accepted: 03/17/2017] [Indexed: 11/18/2022] Open
Abstract
Quantum materials such as antiferromagnets or superconductors are complex in that chemical, electronic, and spin phenomena at atomic scales can manifest in their collective properties. Although there are some clues for designing such materials, they remain mainly unpredictable. In this work, we find that enhancement of transition temperatures in BaFe2As2-based crystals are caused by removing local-lattice strain and electronic-structure disorder by thermal annealing. While annealing improves Néel-ordering temperature in BaFe2As2 crystal (TN = 132 K to 136 K) by improving in-plane electronic defects and reducing overall a-lattice parameter, it increases superconducting-ordering temperature in optimally cobalt-doped BaFe2As2 crystal (Tc = 23 to 25 K) by precipitating-out the cobalt dopants and giving larger overall a-lattice parameter. While annealing improves local chemical and electronic uniformity resulting in higher TN in the parent, it promotes nanoscale phase separation in the superconductor resulting in lower disparity and strong superconducting band gaps in the dominant crystal regions, which lead to both higher overall Tc and critical-current-density, Jc.
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Ziatdinov M, Lim H, Fujii S, Kusakabe K, Kiguchi M, Enoki T, Kim Y. Chemically induced topological zero mode at graphene armchair edges. Phys Chem Chem Phys 2017; 19:5145-5154. [PMID: 28140409 DOI: 10.1039/c6cp08352h] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The electronic and magnetic properties of chemically modified graphene armchair edges are studied using a combination of tight-binding calculations, first-principles modelling, and low temperature scanning tunneling microscopy (STM) experiments. The atomically resolved STM images of the hydrogen etched graphitic edges suggest the presence of localized states at the Fermi level for certain armchair edges. We demonstrate theoretically that the topological zero-energy edge mode may emerge at armchair boundaries with asymmetrical chemical termination of the two outermost atoms in the unit cell. We particularly focus our attention on armchair edges terminated by various combinations of the hydrogen (H, H2) and methylene (CH2) groups. The inclusion of the spin component in our calculations reveals the appearance of π-electron-based magnetism at the armchair edges under consideration.
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Affiliation(s)
- M Ziatdinov
- Department of Chemistry, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan.
| | - H Lim
- Surface and Interface Science Laboratory, RIKEN, Wako, Saitama 351-0198, Japan
| | - S Fujii
- Department of Chemistry, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan.
| | - K Kusakabe
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka 560-8531, Japan
| | - M Kiguchi
- Department of Chemistry, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan.
| | - T Enoki
- Department of Chemistry, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8551, Japan.
| | - Y Kim
- Surface and Interface Science Laboratory, RIKEN, Wako, Saitama 351-0198, Japan
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