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NIST only participates in the February and August reviews. This program involves multimodal imaging techniques that use magnetic resonance imaging (MRI) as either a base or as a complimentary
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, robotics), cyberinfrastructure (e.g., databases, high-performance computing, collaboration tools), and humans (e.g., scientists, engineers, students, managers). The recent interest in Explainable AI (XAI
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Robotic Systems for Smart Manufacturing Program is developing the measurement science needed to enable manufacturers to characterize and understand the performance of robotics systems within
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certainly impact the design of inhibitors towards ERK1/2. We have only scratched the surface of this important problem, and our discoveries to date now set the stage for deeper examination. Needed are further
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for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring of such processes be experimentally validated under highly controlled
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employed. This involves the computational determination of 3-D features of a specimen from a series of their 2-D projections. By carefully preparing the specimen, designing the experimental acquisition, and
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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extensive internal and external collaborations, providing access to a full range of state-of-the-art materials characterization and computational modeling capabilities. The results will have broad
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[3]. [ 1] OpenAI Microscope, a collection of visualizations of every significant layer and neuron of 13 important vision models, URL [ 2] Peter Bajcsy et al., “AI Model Utilization Measurements
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simulation techniques. In addition, simulations and examination of the overall separation process may require computational studies across multiple length scales. key words Modeling; Nanotube; Molecular