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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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identification of spectral features by computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular
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. The research includes experimental measurements on industrially-pertinent materials using a range of advanced methods (various modes of transmission electron microscopy, x-ray/neutron scattering, x-ray
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metrology settings, however, one must understand how models relate to and inform uncertainty estimates. Our current research addresses such problems by combining physics with tools from applied analysis
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processing of tables, extraction of property data from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc
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methods such as formal grammars, logic-based semantic representations, and knowledge graphs to improve the representation of knowledge, the evaluation of AI systems, and classification of documents and
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, Siamak Sattar, David Butry, Steven McCabe, “A Predictive Modeling Approach to Estimating Seismic Retrofit Costs”, Earthquake Spectra, (2019), 36 (2), 579-598. Honglan Huang H., Henry Burton, Siamak Sattar
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called the Fire Dynamics Simulator (FDS) . FDS is a block structured Cartesian LES code for low-speed flow which uses a high-order cutcell-immersed boundary method for complex geometry and is specifically
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will be complemented by computer model simulations using available capabilities based on methods such as density functional theory (DFT). [3] [1] J. Ilavsky, F. Zhang, R.N. Andrews, I. Kuzmenko, P.R
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NIST only participates in the February and August reviews. Project Description:NIST is developing a novel neutron interferometric phase imaging method using a grating-based, far-field interferometer