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for the world. Likewise, U.S. manufacturing depends on its 500,000+ machine tools that make precision parts. However, a major problem with these machines is that their performance, which degrades over time due
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XRD measurements mostly at NIST’s Beamline for Materials Measurements, collaborating with scientists from NIST Gaithersburg, and developing work-flow and data-analysis tools for managing and
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to identify bugs and detect, analyze, prioritize, and resolve or mitigate vulnerabilities. The BF tools and APIs [2] support development of systems for the generation of weakness and vulnerability
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on developing methods, algorithms, data, and tools, to support autonomous experimentation as well as prediction of industry- and/or community-relevant material properties. Keywords Machine Learning; Artificial
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; Augmented intelligence; Machines; Machine tools; Production machines; Semiconductors; Measurement science; Metrology; Sensors; Technology transfer Eligibility citizenship Open to U.S. citizens level Open to
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the combined research of the AEO and the CRP, web-accessible tools are being developed (e.g., the Economic Decision Guide Software – EDGe$ Online Tool) to support community resilience planning. Therefore, strong
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a premier tool for probing atomic dynamics, yet extracting physical insights from experimental data remains a significant computational challenge. Traditional methods—Empirical Force Fields and
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methods that leverage theory, simulation, and machine learning (ML) tools to greatly reduce the expense of creating phase diagrams and mapping formulation stability. [1] Chem. Mater. 2023, 35, 3, 846-852
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materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling
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mostly at NIST’s Beamline for Materials Measurements with the intent of develpoing methods to leverage machine learning and artifical intelligence tools to enhance and automate beamline operations. Some