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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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local disorder as probed by X-ray Absorption Spectroscopy result in emergent material properties Machine-learning driven electrochemical deposition of coatings controlling surface properties
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sensors for measurements before and during manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and
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Advancing the state of the art in measurements of sound, vibration, force, acceleration and velocity
information into neural networks for modeling dynamic systems; use of uncertainty information to improve the performance of sensor-network based measurements employing machine learning; uncertainty
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manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and their processes. Semiconductor manufacturers desire
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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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polyethylene and polypropylene. To overcome this challenge, we have previously shown that utilizing machine learning, real-time NIR measurements can be correlated to molecular architecture sensitive properties
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: Microbiome; Bacteria; Microbiology; Metabolites; Nuclear Magnetic Resonance, Mass-spectrometry, Chemometrics; Multivariate statistics; Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL
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are interested in using Machine Learning and AI techniques to enable autonomous, AI-Driven, experimental research. There are many aspects of this nascent field that require further development. This includes