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and other complex fluids using molecular simulations. In order to make these simulations more computationally feasible, development of coarse-grained models and new Monte Carlo or molecular dynamics
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NIST only participates in the February and August reviews. The fire modeling community is working to develop the tools needed to quantitatively predict material and product flammability behavior
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parameters to improve thermodynamic models of aqueous solutions, (3) development and use of manometry or other methods to determine total dissolved inorganic carbon and partial pressure of aqueous carbon
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of products that incorporate billions of nanoscale features. Recent publications report our scatterfield microscopy techniques in which extensive electromagnetic modeling, instrument characterization, and data
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Laboratory. doi: https://doi.org/10.6028/NIST.TN.2178 . building control; intelligent agents; optimization; data analytics; machine learning; data-driven models; HVAC
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research. https://www.nist.gov/el/goals-programs/high-performance-building-systems References Ng, Lisa C. and W. Vance Payne. Energy use consequences of ventilating a net-zero energy house. Applied Thermal
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dielectric films deposited on graphene using a non-contact microwave technique ( https://dx.doi.org/10.1021/acs.jpcb.9b11622) and monolayer graphene ( https://dx.doi.org/10.1021/acs.jpcb.9b11622 ) as a
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adoption of this manufacturing technology—especially for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring
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clocks used in the past, creating “gappy” data which often strain, or outright violate, the assumptions underlying the statistical models currently used. This project centers around investigating and
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materials databases to be integrated into the NIST-JARVIS (https://jarvis.nist.gov/ ) infrastructure. We work closely with experimental collaborators for validation and focus on releasing software, models