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Integration of Physics and Statistics in Imaging Via Deep Data

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2016 ORAU Annual Meeting of the Council of Sponsoring Institutions
Sergei V. Kalinin Institute for Functional Imaging of Materials
Guiding the design of materials tailored for functionality

Published in: Government & Nonprofit
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Integration of Physics and Statistics in Imaging Via Deep Data

  1. 1. 1 ORNL is managed by UT-Battelle for the US Department of Energy Integration of Physics and Statistics in Imaging Via Deep Data Guiding the design of materials tailored for functionality Sergei V. Kalinin Institute for Functional Imaging of Materials
  2. 2. 2 More than imaging Atomic positions can be determined to <10-pm precision Bond length: Chemical reactivity, catalytic activity Bond angles: Magnetism and transport Configurations and repeating elements? J.J. Guo et al., Nat. Comm. 5, 5389 (2014) Nature 515, 487 (2014)
  3. 3. 3 Dynamic matter: information dimension Static matter Functional matter Dynamic matter Controlled matter Unsupervised learning Theory Correlative learning Image recognition In-situ Control Bigdata Imaging Electronic Structure Molecular Dynamics Multiscale Ab Initio dynamics
  4. 4. 4 Imaging: What do the atoms do? Classical concept • Synthesis • Characterization • Theory • Computation Expanding to include • Data mining • Correlative functional imaging • Local theory-experiment matching of multi-dimensional (multi- modal), spatially and temporally resolved information Institute for Functional Imaging of Materials (IFIM) • Establish synergy between imaging disciplines • Bridge physical imaging with theory via big data and data analytics to design new materials • Leverage ORNL strengths in – Physics and chemistry on the atomic scale in real space – Mesoscale structure and functional probing – Big data and predictive theories Our scientific paradigm is shifting
  5. 5. 5 Approach • Unsupervised learning, clustering, and visualization • Biggest hurdle: Language/ elementary tools 1. Big data: How does it happen? • Physics informed data analytics/ supervised methods • Biggest hurdles: Mathematical framework, scalability of computational tools 2. Deep data: How can we understand? • Feedback and expert/AI systems • Biggest hurdles: Don’t know where to start, but it is possible 3. Smart data: How can we do better? Physics: Why something happens 0. Getting big data: making imaging tools a part of data infrastructure
  6. 6. 6 Level 0: Getting big data 1. Synergy of microscopies 2. Enabling technologies 3. Novel probes Chemistry Materials Science Global Security Biology Environment Biomedical Technology INSTITUTE FOR FUNCTIONAL IMAGING OF MATERIALS Atom Probe Tomography Scanning Probe Microscop y Chemical Imaging Neutron Imaging Optical Imaging Electron Microscopy Mass Spectrometry
  7. 7. Kalinin, Jesse, Proksch, Information Acquisition & Processing in Scanning Probe Microscopy, RD Mag 2008 Data Generation and Utilization in SPM Single frequency methods: Band excitation: We realized we are doing big data • SPM tip confines electric/thermal field in material and probes associated responses • Fundamental physics of stimulus-induced transformation requires high (3,4,5) dimensional measurements -> large data volumes/analysis times • Need approaches to visualize and reduce data (big data) and extract relevant information (deep data)
  8. 8. G-mode: Full Information Recovery Applications: • Fast ferroelectric loop imaging (x7,000 compared to standard method) • Full dynamics in Kelvin Probe Force Microscopy (x1,000 to classical method) • W2 spectroscopic imaging (no classical analogs) Future: • Fast force-distance curve imaging • Detection of spurious and transient phenomena • Variable density imaging A. Belianinov et al., Nat. Comm 6, 6550 (2015) Instrumental limit: photodetector bandwidth (~10 MHz) x DAQ performance (32 Bit) • Single frequency/heterodyne: lock-in compression to ~ 1 kHz • Band excitation: 102 bins at ~ 1 kHz = 100 kHz • G-mode: full streaming at ~10 MHz
  9. 9. 9 Data Generation in Electron Microscopy electron beam Specimen Sub-Å probe Advanced DAQ Fast Direct Electron Detection To scan coils One dimensional excitation signal Complex detection signals • 0D: bright/dark field intensity • 1D: energy loss spectra • 2D: ptychography/diffraction Can be realized on 2D (image) and 3D (focal series, tilt series) spatial grids
  10. 10. 10 Instrumental limit: Electron flux (2 108 e/s) x detector performance (1 Bit/electron) • Detectors: Information per electron? • Storage, visualization, and curating Data Generation in Electron Microscopy
  11. 11. 11 Ptychographic Imaging • The standard STEM is exquisitely tuned to capture material structure (in projection) • However, the transmitted electrons carry far more information than is captured by monolithic detectors: sub-atomic diffraction pattern • Capture full information stream • Emulate monolitic detectors: any geometry • Multivariate analysis Grain boundary in BiFeO3
  12. 12. R. Ishikawa et al., Nano Lett. 14, 1903 (2014) • Can we use induced single atom dynamics to fabricate bulk 3D structures? • Synergy of e-beam writing and advanced feedback and control • 3D atomic fabrication: quantum computing, spintronics, etc. D. Eigler Long long time ago SPM facility far far away Material Sculpting and Electrochemical writing? A. Borisevich S. Jesse Q. He
  13. 13. 13 Level 1: Big Data Unsupervised Learning and classification
  14. 14. Imaging: A. Borisevich Q. He Sample: V. Guilants Image analysis: S. Jesse A. Belianinov
  15. 15. Normal Modes: Analysis of Nearest Neighbors ( ) ( )jkikji waA ωω = PCA transforms the data such that the greatest variance by any projection lies on the first coordinate k-means clustering aims to partition the n observations into k sets (k ≤ n) S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares arg min � � ||𝑥𝑥𝑗𝑗 − 𝜇𝜇𝑖𝑖||2 𝑥𝑥𝑗𝑗∈𝑆𝑆𝑖𝑖 𝑘𝑘 𝑖𝑖=1 Principal Component Analysis K-means Clustering Chemistry Physics
  16. 16. Image Analytics: Enabling the Discovery • Multivariate analysis of atomic shapes and nearest neighborhoods • Local physics and chemistry from connectivity and distortions • Image based analysis for quantitative improvement of Molybdenum–Vanadium based complex oxide catalysts for propane ammoxidation Q He, J Woo, A Belianinov, VV Guliants, A Borisevich; ACS nano, DOI: 10.1021/acsnano.5b00271, (2015)
  17. 17. 15° a b a 1.5 2.3 03 nm -0.01 0.00 0.01 dI/dV(a.u.) Bias (V) STM image of (11) at L-He Superconductive gap map Filtered STM Image Local crystallography The defect preserves lattice continuity, but is associated with change in molar volume and lattice parameter - Guinier-Preston zone. Superconductivity is suppressed at the defect. Local property mapping
  18. 18. 18 Local structure-property coupling Surface atomic structure Tunneling spectral image Structure descriptors: 1. Atom height 2. Molar volume 3. …. Electronic property descriptors: 1. PCA components of spectra 2. Superconductive gap 3. ….
  19. 19. Phase 1 Phase 2 Image Positions Physics Structure 3D 4D 5D Spectra Multivariate Analysis Properties Physics and chemistry on single defect level Identify & Classify Structure Analysis Register & Deconvolute Genomic Library Atomic-Scale Structure and Functionality Need new language: 1. What are structural descriptors? 2. How do we define local symmetry, phases and ferroic variants? 3. How do we introduce and quantify translational symmetry? What do we learn: 1. Structure-property relationship on single atom, molecule, and defect level 2. Libraries of structure-property relationships 3. Feedback to theory through microscopic degrees of freedom Scattering methods: completeness of library Macroscopic properties: averaging rules
  20. 20. 20 Imaging to materials by design “Stochastic” library Future: • Libraries of preferred local configurations: what is relevant • Structural + functional imaging: stochastic combinatorial libraries • Theory based prediction Classical approach: Synthesis → Characterization → Theory Need: • Functional probes • High-resolution structural imaging • Theoretical models • Big/Deep/smart data Tm γ Y P Ρ … Bulk Crystal Chemical Space Functional Properties φ (x,y,V,…) Y (x,y,V,…) P (x,y,V,…) … Big data from imaging:
  21. 21. 21 Level 2: Deep Data 1. “Theoretical microscope” 2. Physics-constrained un-mixing 3. Inverse problems Transition from correlative to causative analysis
  22. 22. Atomistic Imaging Atomistic Simulation Fundamental Science via Local Degrees of Freedom • Can we complement experiment by theory to visualize invisible degrees of freedom and extract functionalities of interest? • Can we refine and improve theory by factoring in experimental data (via Bayesian inference) • Can we develop approach to extract relevant macroscopic parameters from experiment and simulations (e.g. via Fischer information) Theoretical microscope Local functionalities calculated from observables Improved theory Bayesian inference
  23. 23. Physics-constrained un-mixing Needs Bottom electrode CFO BFO A R1 R2 R2 R1 4D dataset I = f(x, y, V, Vp) 0 1 2 -5 0 5 time (s) Voltage(V) 0 1 Current(nA) Vp Current-voltage curves at each location x y Fitting to physical models Extracting Physical meaning Eigenvector 1 Loading 1 Eigenvector 2 Loading 2 Need: • Un-mixing with user-defined constraints on the endmembers or loading maps • Combined spatial and spectral unmixing • Incorporate physics (symmetry, non-negativity, material parameter/models, etc.) Strelcov et al, ACS Nano 2014, 2015 Vasudevan et al submitted Real space Spectral space
  24. 24. Mesoscale Structure and Dynamics: Inverse Problem Can we learn: • Free energy expansions • Thermodynamics • Universality classes • Frozen disorder • Reaction/diffusion kinetics Mesoscale dynamics • Reaction/transport • Ginzburg-Landau Theory • Molecular Dynamics Pt nanoparticle growth under e-beam R. Unocic Source image Binary image Detected particles COMSOL Multiphysics PDA solution Concentration Matlab • Exp. boundary detection • Data comparison Particles boundary Simulated concentration field cD t c ∆= ∂ ∂ Growth controlled by Pt transport: Particle boundary conditions: qc dn dc boundary −= kcvgrowth =Local growth velocity: Simulation workflow
  25. 25. STEM/EELS, SPM Transfer files to HPC storage and convert data to HDF5 format Massively parallel Image processing/ feature detection Massively parallel electronic structure calculation • High throughput image capture • Multi-modal: -High-angle annular dark field (HAADF) detector - Electron energy loss spectroscopy • O(1000) of images per experiment • Data motion via BBCP/GridFTP • Data conversion from DM3 stacks to HDF5 slices • Standard data format (HDF5) • Data layout conducive to HPC algorithms • Denoise Image • Identify atoms • Identify lattice • Thousands of images/sec • Built on MPI+Fortran • Near linear scaling – some limitations as file counts get extremely large • Massively parallel study of hundreds to thousands of individual configurations • Calculation of electronic structure • Based on Density Functional Theory • 0(1000) of configurations Minutes Seconds MinutesSeconds Need: Supporting real-time image analytics
  26. 26. Level 3: Smart Data - Supervised Learning - Context - History
  27. 27. Expert Control Automatic Expert System Decision making User Model Experimental data Timeline -10 -5 0 5 10 -2.8 -1.4 0.0 1.4 2.8 PFMSignal(a.u.) Bias (V) From Human Expert to Automatic Systems Future: • Automated analysis of routine data • Identification of anomalies • Initial training of new practitioners • Data centers: information based on knowledge • Synthesis of expertise: factor in human expert knowledge • Context search: published results data mining/social networks
  28. 28. Understanding Data J. Electron. Imaging. 2012;21(3):033010-1-033010-13. doi:10.1117/1.JEI.21.3.033010 Statistical measures of orientation of texture for the detection of architectural distortion in prior mammograms of interval-cancer Smart data: Google car, cancer screening, expert systems
  29. 29. ResearcherInstrument Control/data acquisition Community • Social networking/education • Publications/citations 1. Only small fraction of data stream from the instrumentation is captured 2. Only small fraction of captured data is analyzed, interpreted, and put in the context 3. Human-machine interaction during acquisition is often slow and can be non-optimal 4. Human interpretation of data is limited: bias and ignoring serendipity 5. Information propagation and concept evolution in scientific community is extremely slow and affected by non-scientific factors Classical Instrumental Research Paradigm
  30. 30. 1. Multiple geographically-distributed data generation node 2. Full capture of instrumental data stream 3. Coordination of protocols and data/metadata across the cloud 4. Cloud-based processing and dimensionality reduction 5. Community-wide analytics Cloud-Based Imaging: Integrated Instrumental Network
  31. 31. 31 Goal: guide the design of materials tailored for functionality via probing, understanding, and designing local structure-property relationships on atomic and nanometer level Means: • Synergy and coordination between imaging disciplines • Linking theory and imaging on the level of microscopic degrees of freedom via data analytics • Big, deep, and smart data in materials exploration and design Institute for Functional Imaging of Materials Static Functional Dynamic Controlled Unsupervised learning Theory Correlative learning Image recognition In-situ control BigdataImaging Electronic Structure Molecular Dynamics Multiscale Ab Initio New probesNew analysis New control
  32. 32. 32 Thank you for coming!

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