TechConnect World 2020
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Nanotech 2020
 
 

Machine Learning for Materials Characterization and Imaging: Special Focus Session

Nanoscale Materials Characterization

Call for Abstract - due April 10 »

Symposium Co-Chairs

Greg HaugstadGreg Haugstad
Technical Staff Member & Director, Characterization Facility (CharFac)
University of Minnesota

Dalia YablonDalia Yablon
Technical Program Chair
TechConnect World Innovation Conference

Key Speakers

Huolin XinArtificially Intelligent Transmission Electron Microscopy
Huolin Xin
Assistant Professor, Department of Physics and Astronomy, University of California, Irvine

Maxim A. ZiatdinovCorrelative and causal machine learning in scanning probe and electron microscopy
Maxim A. Ziatdinov
Research Scientist, Oak Ridge National Laboratory

Mary ScottMary Scott
Assistant Professor
University of California, Berkeley

Aaron Gilad KusneAutonomous Synchrotron X-ray Diffraction for Phase Mapping and Materials Optimization
Aaron Gilad Kusne
Researcher, National Institute of Standards and Technology

Maria K. Y. ChanMachine Learning for Materials Characterization and Imaging
Maria K. Y. Chan
Scientist, Center for Nanoscale Materials, Argonne National Laboratory

Mathew J. CherukaraReal-time 3D Coherent Diffraction Data Inversion Through Deep Learning
Mathew J. Cherukara
Assistant Scientist, Center for Nanoscale Materials, ​Argonne National Lab

The purpose of this symposium is to explore the application of machine learning to characterization with an emphasis on microscopy methods such as electron microscopy, probe microscopy, optical microscopy, and other imaging techniques. A natural point at the nexus of ML and such methods is image analysis and processing. Modern imaging systems have the further capability to generate data cubes, where the third dimension is a kind of spectral information. Thus in seeking characteristic "signatures" during data analysis one is exploring a much richer terrain, not simply looking for features or patterns in pictures. In addition to faster, "deeper", and more powerful image processing, additional issues that will be addressed include a broader role for ML improving our current capabilities and even enabling new modes and techniques.

 
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Symposium Sessions

Monday June 29

10:30Machine Learning for Optical and Radiative Microscopy
1:30Machine Learning for Probe and Electron Microscopy

Tuesday June 30

4:00Materials Characterization - Posters
4:00Machine Learning for Materials Characterization & Imaging - Posters

Symposium Program

Monday June 29

10:30Machine Learning for Optical and Radiative Microscopy
Session chair: Greg Haugstad, University of Minnesota, Dalia Yablon, TechConnect, US
Autonomous Synchrotron X-ray Diffraction for Phase Mapping and Materials Optimization
A.G. Kusne, National Institute of Standards & Technology, US
Real-time 3D Coherent Diffraction Data Inversion Through Deep Learning
M. Cherukara, H. Chan, T. Zhou, Y. Nashed, S. Sankaranarayanan, M. Holt, R. Harder, Argonne National Lab, US
Machine learning based detection and deep learning based image inpainting of preparation artefacts in micrographs
A. Jansche, A.K. Choudhary, T. Bernthaler, G. Schneider, Aalen University, DE
Improvement of Oil Spill Mapping from Satellite Image Using Directional Median Filtering with Articicial Neural Network
S.H. Park, H.S. Jung, University of Seoul, KR
Application of deep convolutional neural networks (DCNN) in materials microscopy for the automated detection of defects
O. Badmos, A. Kopp, D. Hohs, R. Büttner, T. Bernthaler, G. Schneider, Hochschule Aalen, DE
Machine Learning for Materials Characterization and Imaging
M.K.Y. Chan, Argonne National Laboratory, US
1:30Machine Learning for Probe and Electron Microscopy
Session chair: Dalia Yablon, TechConnect, US, Greg Haugstad, University of Minnesota, US
TBA
M. Scott, University of California, Berkeley, US
Artificially Intelligent Transmission Electron Microscopy
H. Xin, University of California, Irvine, US
Correlative and causal machine learning in scanning probe and electron microscopy
M. Ziatdinov, Oak Ridge National Laboratory, US
Opportunities in Machine Learning for Atomic Force Microscopy
I. Chakraborty, D. Yablon, Stress Engineering Services, Inc., US
Intermodulation AFM a novel multifrequency technique for material insight
D. Forchheimer, Intermodulation Products AB, SE
Fourier-reconstructed force fingerprints in AFM: machine learning for novel contrast
G. Haugstad, A. Avery, R. Rahn, S. Hubig, B. Luo, H.-S. Lee, A. McCormick, D. Forschheimer, University of Minnesota, US

Tuesday June 30

4:00Materials Characterization - Posters
Effects of Ga–Cr substitution on structural and magnetic properties of hexaferrite (BaFe12O19) synthesized by sol–gel auto-combustion route
I. Ali, Higher Education Department, Government of Punjab, PK
X-ray imaging of colloidal packing
Y. Kim, G. Oh, W. Jung, B.M. Weon, SungKyunKwan University, KR
Portable material analyser
V. Vishnyakov, University of Huddersfield, UK
Line Confocal Imaging Technology in In-situ 3D, 2D and Tomographic Characterization of Specular and Transparent Parts, Assemblies and Continuous Products
J. Saily, FocalSpec - LMI Technologies (USA), Inc., US
Secondary Ion Mass Spectrometry Image Depth Profiling for Visualizing the Uptake and Biodistribution of Gold Nanoparticles in Caenorhabditis elegans
M.E. Johnson, J. Bennett, A.R. Montoro Bustos, S.K. Hanna, A. Kolmakov, N. Sharp, E.J. Petersen, P.E. Lapasset, C.M. Sims, K.E. Murphy, B.C. Nelson, National Institute of Standards & Technology, US
Spectral Characterization of Tin Dioxide for Gas-Sensing Applications
B. Concepcion, H. Alghamdi, S. Baliga, P. Misra, Howard University, US
Phenomenological modeling of apparent viscosity based on the degree of cure of an EPDM elastomer
S. Gómez-Jimenez, A.M. Becerra-Ferreiro., E. Jareño-Betancourt, J. Vázquez-Penagos, Autonomous University of Zacatecas, MX
A Self-Driving Laboratory for Accelerated Materials Discovery
C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, The University of British Columbia, CA
Characterization and Growth Mechanism of APCVD Grown 2D monolayer WS2
M.B. Azim, M. Adachi, Simon Fraser University, CA
4:00Machine Learning for Materials Characterization & Imaging - Posters
Machine learning for microstructures classification in functional materials
A.K. Choudhary, A. Jansche, O. Badmos, T. Bernthaler, G. Schneider, Aalen University, DE
A Machine Learning Driven Damage Quantification Algorithm in moisture-contaminated composites.
R.D. Guha, North Carolina State University, US
Application of Savitzky-Golay(SG) filter in image processing
S. Karmakar, S. Karmakar, Farmingdale State College- State University of New York, US

Call for Abstract - due April 10 »

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