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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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process control measurements, and utilizing appropriate statistical analyses to understand the assay results and their uncertainties. This process should lead to improvements in the comparability and
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infrastructure, and experimental automation to materials characterization (metrology) methods across all portions of the structure-processing-properties-performance relationship. Specific group research focus
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processes over such an extended time range is a formidable task for conventional molecular dynamics. We have developed a mathematical technique for simulation of phonon transport in nanomaterials based
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NIST only participates in the February and August reviews. Many industrial processes generate carbon dioxide as a by-product, which is released to the atmosphere and contributes to global warming
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
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, interfacial phenomena, and associated transduction processes, often at the nanoscale that allow detection. A wide variety of sensing principles (chemiresistive, capacitive, electrochemical, FET, mass loading
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an understanding of the basic properties of atom-based solid-state and nanoscale systems, optics on the nanoscale (nano-optics) quantum processes in atom-scale and nanoscale systems with optical fields, and quantum
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disruptive events. More research is needed to understand the planning, protective, and recovery processes of its highly interdependent physical, social, and economic systems. In particular, advancements in
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sample preparation equipment (including cross-sections) and also an ability to fabricate prototype devices using electron lithography. The current topics of interest include the process development and