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molecules (e.g., CH4, C2H6, CO2). These materials undergo guest-induced structural transformations, offering a unique mechanism for high-density gas storage and highly selective chemical separations. Our
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driven flows; Combustion; Computational fluid dynamics; Fire modeling; Heat transfer; Large eddy simulation; Numerical combustion; Thermal radiation; Turbulent flows; Eligibility citizenship Open to U.S
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301.975.4579 Description As the demand for high resolution, high content imaging increases, the cost and challenges of acquiring, storing, processing, and analyzing today’s very large imaging data sets are even
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303.497.4598 Description The Applied Chemicals and Materials Division (ACMD) of the NIST Material Measurement Laboratory (MML) performs numerical modeling to advance laboratory measurements of exhaled breath
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-enhanced laser-based methods, optical spectroscopies (infrared, visible, ultraviolet, Raman, light scattering), gas chromatography/mass spectroscopy (GC/MS), high-performance liquid chromatography (HPLC
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of interatomic potential. This research will focus on performing high throughput calculations to explore dynamic property predictions across interatomic potentials, temperatures, and atomic configurations
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ferroelectrics, thermoelectrics, and nanomaterials. Computational modeling approaches include high-throughput computation (see jarvis.nist.gov), predictive tight-binding analysis (see github.com/usnistgov
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-film samples on waveguide interfaces and gas phase samples over temperature ranges from 1.7 K to 350 K. The experimental results are modeled using high-level quantum mechanical methods (DFT/MP2/MRCI
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challenging. We seek to address this measurement problem by developing a coherent strategy for integrating inputs from several critical experimental techniques to perform fully atomistic structural refinements
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experimentally. However, important challenges remain, such as transition-metal compounds and floppy or tautomerizing molecules. Determining the quantitative uncertainties associated with high-level predictions is