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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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technology. Reference Lee CH, et al: Exploiting dimensionality and defect mitigation to create tunable microwave dielectrics. Nature 502: 532-536, 2013 key words Electronics; Microelectronics; Machine learning
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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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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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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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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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machine learning frameworks to tackle various realistic challenges to achieve desired model performance to meeting real-life firefighting needs. Some of our latest works to overcome problems associated with
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NIST RMs in this class, with widely-used benchmark germline variant calls for seven human cell lines [1]. Artificial intelligence and machine learning hold promise to automate and improve integration
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properties, as well as the absence of commonly accepted machine-readable data formats, requires tremendous human efforts to acquire the desired information from scientific papers, analyze the quality and
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of CO2 into fuels, direct air capture, machine learning for large-scale material screening etc. Working closely with experimental teams at NIST, our computational efforts complement the experimental