This year's symposium will focus on innovations in nanoscale materials characterization including new method development as well as industry-specific applications. Submissions are also invited for special sessions focused on machine learning for materials characterization and imaging (in conjunction with the AI TechConnect Conference); and on industrial applications of x-ray scattering methods.
Back to Top ↑2021 Symposium Sessions |
| Monday October 18 |
| 10:30 | Machine Learning for Microscopy |
| 1:30 | AI for Characterization & Manufacturing |
| Tuesday October 19 |
| 8:30 | Advanced Materials Track Keynotes |
| 1:30 | Advances in Image Analysis |
| Wednesday October 20 |
| 8:30 | AI Track Keynote |
| 1:30 | User Facilities for Industrial Participation |
| 4:00 | Materials Characterization - Posters |
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2021 Symposium Program |
| Monday October 18 |
|
| 10:30 | Machine Learning for Microscopy | Baltimore 1 |
| Session chair: Greg Haugstad, University of Minnesota |
| 10:30 | Automated Analysis of Transmission Electron Microscopy Images for Characterization of Dynamic Material Systems
J.P. Horwath, D.J. Groom, P.J. Ferreira, E.A. Stach, University of Pennsylvania, US |
| 10:55 | Rapid DNA Origami Nanostructure Detection and Classification Using the YOLOv5 Deep Convolutional Neural Network, pp. 134-137
M. Chiriboga, C.M. Green, D.A. Hastman, D. Mathur, Q. Wei, I.L. Medintz, S.A. Díaz, R. Veneziano, United States Naval Research Laboratory, US |
| 11:15 | Using machine learning to probe classification and correlation AFM images
I. Chakraborty, Stress Engineering Services, Inc, US |
| 11:35 | Machine learning for microstructures classification in functional materials, pp. 114-117
A.K. Choudhary, A. Jansche, Grubesa Tvrtko, T. Bernthaler, G. Schneider, Aalen University, DE |
| 11:55 | Machine learning based detection and deep learning based image inpainting of preparation artefacts in micrographs, pp. 118-121
A. Jansche, A.K. Choudhary, T. Bernthaler, G. Schneider, Aalen University, DE |
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| 1:30 | AI for Characterization & Manufacturing | Baltimore 1 |
| Session chair: Greg Haugstad, University of Minnesota & Grace Gu, University of California, Berkeley |
| 1:30 | Materials Informatics for Simultaneous Design of Alloy Chemistry and AM Process
S. Broderick, University of Buffalo, US |
| 1:55 | Machine Learning for In-Water Inspection of Submarine Hull Coatings
M. An, J. Cipolla, A. Shakalis, B. Hiriyur, R. Tolimieri, Prometheus Inc., US |
| 2:15 | Gamma-Ray Raster Imaging with Robotic Data Collection, pp. 109-110
W. Wells, T. Aucott, M. Siddiqi, Savannah River National Laboratory, US |
| 2:35 | A New Method for Atmospheric Correction of Satellite Data
D. Groeneveld, Advanced Remote Sensing, Inc., US |
| 2:55 | Real-Time Porosity Prediction for Metal Additive Manufacturing using Convolutional Neural Networks
W. Young, S. Ho, S. Al Jufout, M. Mozumdar, M. Buchholz, W. Zhang, K. Dajani, California State University, Long Beach, US |
| 3:15 | Forecasting and Decision Impact Analysis from Ripple Effects of Behaviors
B. Frutchey, BigBear.ai, US |
| 3:35 | Leveraging Machine Learning to Predict Public Transportation Arrival Times
P. Reshetova, W. Ruzicka, EastBanc Technologies, US |
| Tuesday October 19 |
|
| 8:30 | Advanced Materials Track Keynotes | Woodrow Wilson D |
| Session chair: Martin Poitzsch, Aramco Americas |
| 8:30 | Controlling light with plasmonic materials
J. Fontana, Naval Research Laboratory, US |
| 9:00 | TBA
A. Scherer, California Institute of Technology, US |
| 9:30 | Nanoscale carbon for print-in-place and recyclable electronics
A.D. Franklin, Duke University, US |
|
| 1:30 | Advances in Image Analysis | Annapolis 3 |
| Session chair: Alex Norman, Modern Meadow & Greg Haugstad, University of Minnesota |
| 1:30 | Advanced Analytical Approaches for Research and Development of Plant-Based Food Ingredients
M. Crowe, Ingredion, US |
| 1:55 | X-ray computed tomography for materials characterization: Transformation by machine learning
A. Takase, Rigaku Corporation, US |
| 2:20 | Development of innovative X-ray scattering and scanning probe microscopy software for materials imaging
G. Yu, University of Minnesota, US |
| 2:45 | Microwave Sensing of Water-Cut in Production Fluids
J. Oliverio Alvarez, Aramco Services Company: Aramco Research Center -- Houston, US |
| 3:05 | Probing the time-temperature relationship of mechanical properties in polymer composites
B. Pittenger, S. Osechinskiy, J. Thornton, S. Loire, T. Mueller, Bruker Nano, US |
| 3:30 | Nanoscale Scanning Probe Metrology for a Non-Flat World: How to measure sub-nanometer roughness on complex geometries and large samples
D. Griffin, E. Nelson, C. Newcomb, Nanosurf, US |
| 3:55 | Intermodulation AFM with machine learning
D. Forchheimer, Intermodulation Products AB, SE |
| 4:20 | Physical interpretations of multimodal AFM contrast on soft materials
G. Haugstad, University of Minnesota, US |
| Wednesday October 20 |
|
| 8:30 | AI Track Keynote | Woodrow Wilson D |
| Session chair: Richard Ross, 3M Company & Peter Koenig, Procter & Gamble |
| 8:30 | Closed-loop autonomous combinatorial experimentation for streamlined materials discovery
I. Takeuchi, University of Maryland, US |
| 9:00 | From atoms to emergent mechanisms with information bottleneck and diffusion probabilistic models
P. Tiwary, University of Maryland, US |
| 9:30 | Deep Learning for Linking Chemical and Biological Space in Small Molecules and Macromolecules
A. Shehu, George Mason University, US |
|
| 1:30 | User Facilities for Industrial Participation | Azalea 1 |
| Session chair: Greg Haugstad, University of Minnesota |
| 1:30 | Industrial Access to the Penn State Materials Characterization Lab
J. Shallenberger, Penn State University, US |
| 1:55 | NIST, Neutrons, and Next Gen Product Development
R. Jones, National Institute of Standards and Technology, US |
| 2:20 | Analytical Resources for Industry in an Academic Institution
J. Shu, Cornell Center for Materials Research, US |
| 2:45 | Neutron Imaging and Far-Field Interferometry
K. Weigandt, National Institute of Standards and Technology, US |
| 3:10 | Harvard Center for NanoScale Systems: An Epicenter for Interdisciplinary Quantum and Nano Science Research and Technologie
W. Wilson, Harvard University, US |
|
| 4:00 | Materials Characterization - Posters | Expo Hall AB |
| Enhanced Corrosion Detection By Means of Multispectral Imaging and Postprocessing Techniques, pp. 35-38
W.V. Giegerich, D.C. Fedorishin, L. Forte III, L. Christie, P.J. Schneider, K.W. Oh, University at Buffalo, US |
| Vascular Test Phantom Metrology For Use In Photoacoustic Applications, pp. 31-34
L.B. Christie, W.V. Giegerich, P. Schneider, K.W. Oh, University at Buffalo, US |
| Characterization of Perovskite Solar Cells using Spectroscopic Ellipsometry
M. Fernanda Villa-Bracamonte, J.R. Montes-Bojorquez, A. Ayon, University of Texas at San Antonio, US |
|
Greg Haugstad
Intermodulation AFM with machine learning
Development of innovative X-ray scattering and scanning probe microscopy software for materials imaging
NIST, Neutrons, and Next Gen Product Development
Neutron Imaging and Far-Field Interferometry
X-ray computed tomography for materials characterization: Transformation by machine learning
Probing the time-temperature relationship of mechanical properties in polymer composites
Advanced Analytical Approaches for Research and Development of Plant-Based Food Ingredients
Nanoscale Chemical Analysis using AFM-IR
Analytical Resources for Industry in an Academic Institution
Using machine learning to probe classification and correlation AFM images
