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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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our data infrastructure is set up so that all microscopy datasets from all users are collected and archived at a central file location. In 2020, we have begun curating and labeling microscopy datasets
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Development of Hyperspectral Raman Imaging for Biology and Medicine: Optical Platform and Data Mining Methods NIST only participates in the February and August reviews. Molecules vibrate with
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testing facility, supported by powerful materials modeling expertise and capabilities (ABAQUS and COMSOL). The link between microstructure and properties will be established through the NIST Precision
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opportunity location 50.73.21.B8164 Gaithersburg, MD NIST only participates in the February and August reviews. Advisers name email phone Michael Frank Link michael.f.linK@nist.gov 970.556.3133 Dustin Glen
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.; McLinden, M. O., Nuclear Magnetic Resonance (NMR) Spectroscopy for the in situ Measurement of Vapor-Liquid Equilibria. J. Chem. Engr. Data 2020, https://pubs.acs.org/doi/10.1021/acs.jced.0c00113 . Nuclear
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on sources, detectors, and timing synchronization systems that can enable entanglement distribution over metropolitan and long-haul fiber links. Beyond entanglement sources and synchronization systems, we have
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. References [1] Ketan S. Khare and Frederick R. Phelan Jr., "Quantitative Comparison of Atomistic Simulations with Experiment for a Cross-Linked Epoxy: A Specific Volume–Cooling Rate Analysis," Macromolecules
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the continuing increase in speed and capability of commodity graphics processors, immersive visualization offers increasing opportunities to express scientifically meaningful results. Data at NIST spans a wide
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strain rates and temperatures. Identifying material properties and constitutive model parameters from the integrated data sets currently focuses on the finite element model updating (FEMU) method, but