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driven self-assembly in an effort to elucidate the underlying thermodynamic and kinetic effects that control the speed, yield, and complexity of self-assembled nanostructures. Our research effort involves
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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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, process, or part qualification and providing benchmarking datasets for model validation to support industry adoption and standards development of metal BJAM. NIST has researched other AM technologies
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