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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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function; (2) explore available algorithms for image post-processing to recover original information about the visual environment; (3) explore scientific applications of new imaging methods; and (4) develop
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and signal-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by
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-deconvolution algorithms that can account for peak asymmetry due to imperfect shims; the use of spatially selective or multidimensional NMR methods; and the development of reference materials, especially for gas
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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