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Liu Y, Morozovska AN, Eliseev EA, Kelley KP, Vasudevan R, Ziatdinov M, Kalinin SV. Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials. PATTERNS (NEW YORK, N.Y.) 2023; 4:100704. [PMID: 36960442 PMCID: PMC10028429 DOI: 10.1016/j.patter.2023.100704] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/18/2022] [Accepted: 02/09/2023] [Indexed: 03/12/2023]
Abstract
Using hypothesis-learning-driven automated scanning probe microscopy (SPM), we explore the bias-induced transformations that underpin the functionality of broad classes of devices and materials from batteries and memristors to ferroelectrics and antiferroelectrics. Optimization and design of these materials require probing the mechanisms of these transformations on the nanometer scale as a function of a broad range of control parameters, leading to experimentally intractable scenarios. Meanwhile, often these behaviors are understood within potentially competing theoretical hypotheses. Here, we develop a hypothesis list covering possible limiting scenarios for domain growth in ferroelectric materials, including thermodynamic, domain-wall pinning, and screening limited. The hypothesis-driven SPM autonomously identifies the mechanisms of bias-induced domain switching, and the results indicate that domain growth is ruled by kinetic control. We note that the hypothesis learning can be broadly used in other automated experiment settings.
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Affiliation(s)
- Yongtao Liu
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Corresponding author
| | - Anna N. Morozovska
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine
| | - Eugene A. Eliseev
- Institute of Physics, National Academy of Sciences of Ukraine, 46, pr. Nauky, 03028 Kyiv, Ukraine
- Institute for Problems of Materials Science, National Academy of Sciences of Ukraine, Krjijanovskogo 3, 03142 Kyiv, Ukraine
| | - Kyle P. Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
| | - Rama Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Corresponding author
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37922, USA
- Corresponding author
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2
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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3
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Ziatdinov M, Ghosh A, Wong CY, Kalinin SV. AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00555-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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4
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Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization. Theor Chem Acc 2021. [DOI: 10.1007/s00214-021-02726-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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5
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Oxley MP, Yin J, Borodinov N, Somnath S, Ziatdinov M, Lupini AR, Jesse S, Vasudevan RK, Kalinin SV. Deep learning of interface structures from simulated 4D STEM data: cation intermixing vs. roughening. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/aba32d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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6
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Mills K, Ryczko K, Luchak I, Domurad A, Beeler C, Tamblyn I. Extensive deep neural networks for transferring small scale learning to large scale systems. Chem Sci 2019; 10:4129-4140. [PMID: 31015950 PMCID: PMC6460955 DOI: 10.1039/c8sc04578j] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 02/28/2019] [Indexed: 12/15/2022] Open
Abstract
We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling. We use a form of domain decomposition for training and inference, where each sub-domain (tile) is comprised of a non-overlapping focus region surrounded by an overlapping context region. The size of these regions is motivated by the physical interaction length scales of the problem. We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets. In the latter, an EDNN was able to make total energy predictions of a 60 atoms system, with comparable accuracy to density functional theory (DFT), in 57 milliseconds. Additionally EDNNs are well suited for massively parallel evaluation, as no communication is necessary during neural network evaluation. We demonstrate that EDNNs can be used to make an energy prediction of a two-dimensional 35.2 million atom system, over 1.0 μm2 of material, at an accuracy comparable to DFT, in under 25 minutes. Such a system exists on a length scale visible with optical microscopy and larger than some living organisms.
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Affiliation(s)
- Kyle Mills
- University of Ontario Institute of Technology , Oshawa , Ontario , Canada . ;
| | - Kevin Ryczko
- University of Ottawa , Ottawa , Ontario , Canada
| | - Iryna Luchak
- University of British Columbia , Vancouver , British Columbia , Canada
| | - Adam Domurad
- University of Waterloo , Waterloo , Ontario , Canada
| | - Chris Beeler
- University of Ontario Institute of Technology , Oshawa , Ontario , Canada . ;
| | - Isaac Tamblyn
- University of Ontario Institute of Technology , Oshawa , Ontario , Canada . ; .,University of Ottawa , Ottawa , Ontario , Canada.,National Research Council Canada , Ottawa , Ontario , Canada
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7
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Vasudevan RK, Choudhary K, Mehta A, Smith R, Kusne G, Tavazza F, Vlcek L, Ziatdinov M, Kalinin SV, Hattrick-Simpers J. Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics. MRS COMMUNICATIONS 2019; 9:10.1557/mrc.2019.95. [PMID: 32166045 PMCID: PMC7067066 DOI: 10.1557/mrc.2019.95] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 07/03/2019] [Indexed: 05/14/2023]
Abstract
The use of advanced data analytics and applications of statistical and machine learning approaches ('AI') to materials science is experiencing explosive growth recently. In this prospective, we review recent work focusing on generation and application of libraries from both experiment and theoretical tools, across length scales. The available library data both enables classical correlative machine learning, and also opens the pathway for exploration of underlying causative physical behaviors. We highlight the key advances facilitated by this approach, and illustrate how modeling, macroscopic experiments and atomic-scale imaging can be combined to dramatically accelerate understanding and development of new material systems via a statistical physics framework. These developments point towards a data driven future wherein knowledge can be aggregated and used collectively, accelerating the advancement of materials science.
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Affiliation(s)
- Rama K. Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Kamal Choudhary
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Apurva Mehta
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025
| | - Ryan Smith
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Gilad Kusne
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Francesca Tavazza
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Lukas Vlcek
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Maxim Ziatdinov
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Sergei V. Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge TN 37831, USA
| | - Jason Hattrick-Simpers
- Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899
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8
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Mills K, Tamblyn I. Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models. Phys Rev E 2018; 97:032119. [PMID: 29776084 DOI: 10.1103/physreve.97.032119] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Indexed: 11/07/2022]
Abstract
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4×4 Ising model. Using its success at this task, we motivate the study of the larger 8×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.
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Affiliation(s)
- Kyle Mills
- Department of Physics, University of Ontario Institute of Technology, Oshawa, Ontario, Canada
| | - Isaac Tamblyn
- Department of Physics, University of Ontario Institute of Technology, Oshawa, Ontario, Canada and Department of Physics, University of Ottawa & National Research Council of Canada, Ottawa, Ontario, Canada
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9
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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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10
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Wetzel SJ. Unsupervised learning of phase transitions: From principal component analysis to variational autoencoders. Phys Rev E 2017; 96:022140. [PMID: 28950564 DOI: 10.1103/physreve.96.022140] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Indexed: 11/07/2022]
Abstract
We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders. They are applied to Monte Carlo-sampled configurations and have, a priori, no knowledge about the Hamiltonian or the order parameter. We find that the most promising algorithms are principal component analysis and variational autoencoders. Their predicted latent parameters correspond to the known order parameters. The latent representations of the models in question are clustered, which makes it possible to identify phases without prior knowledge of their existence. Furthermore, we find that the reconstruction loss function can be used as a universal identifier for phase transitions.
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Affiliation(s)
- Sebastian J Wetzel
- Institut für Theoretische Physik, Universität Heidelberg, Philosophenweg 16, 69120 Heidelberg, Germany
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11
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Kalinin SV, Strelcov E, Belianinov A, Somnath S, Vasudevan RK, Lingerfelt EJ, Archibald RK, Chen C, Proksch R, Laanait N, Jesse S. Big, Deep, and Smart Data in Scanning Probe Microscopy. ACS NANO 2016; 10:9068-9086. [PMID: 27676453 DOI: 10.1021/acsnano.6b04212] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.
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Affiliation(s)
| | | | | | | | | | | | | | - Chaomei Chen
- College of Computing and Informatics, Drexel University , Philadelphia, Pennsylvania 19104, United States
| | - Roger Proksch
- Asylum Research, an Oxford Instruments Company , Santa Barbara, California 93117, United States
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12
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Belianinov A, Vasudevan R, Strelcov E, Steed C, Yang SM, Tselev A, Jesse S, Biegalski M, Shipman G, Symons C, Borisevich A, Archibald R, Kalinin S. Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets. ADVANCED STRUCTURAL AND CHEMICAL IMAGING 2015; 1:6. [PMID: 27547705 PMCID: PMC4977326 DOI: 10.1186/s40679-015-0006-6] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2015] [Accepted: 04/21/2015] [Indexed: 11/10/2022]
Abstract
The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.
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Affiliation(s)
- Alex Belianinov
- 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
| | - Rama Vasudevan
- 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
| | - Evgheni Strelcov
- 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
| | - Chad Steed
- Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Sang Mo Yang
- 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
- Center for Correlated Electron Systems, Institute for Basic Science (IBS), Seoul, 151-747 South Korea
- Department of Physics and Astronomy, Seoul National University, Seoul, 151-747 South Korea
| | - Alexander Tselev
- 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
| | - Stephen Jesse
- 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
| | - Michael Biegalski
- The Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Galen Shipman
- Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Computer, Computational, and Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545 USA
| | - Christopher Symons
- Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Albina Borisevich
- Institute for Functional Imaging of Materials, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
- Materials Sciences and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA
| | - Rick Archibald
- 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
| | - Sergei Kalinin
- 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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13
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Normando PG, Nascimento RS, Moura EP, Vieira AP. Microstructure identification via detrended fluctuation analysis of ultrasound signals. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2013; 87:043304. [PMID: 23679545 DOI: 10.1103/physreve.87.043304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Indexed: 06/02/2023]
Abstract
We describe an algorithm for simulating ultrasound propagation in random one-dimensional media, mimicking different microstructures by choosing physical properties such as domain sizes and mass densities from probability distributions. By combining a detrended fluctuation analysis (DFA) of the simulated ultrasound signals with tools from the pattern-recognition literature, we build a Gaussian classifier which is able to associate each ultrasound signal with its corresponding microstructure with a very high success rate. Furthermore, we also show that DFA data can be used to train a multilayer perceptron which estimates numerical values of physical properties associated with distinct microstructures.
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Affiliation(s)
- Paulo G Normando
- Departamento de Engenharia Metalúrgica e de Materiais, Universidade Federal do Ceará, 60455-760, Fortaleza, CE, Brazil
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