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to predict materials properties is essential to improve materials design methods. This research will focus on the development and integration of first principle calculations; atomistic simulations; and/or
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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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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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-process densification. Complementary computational model simulation capabilities are also available. [1] J. Ilavsky, F. Zhang, R.N. Andrews, I. Kuzmenko, P.R. Jemian, L.E. Levine & A.J. Allen; J. Appl
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formulation must be linked seamlessly. We will develop a Green’s function (GF) method for modeling color centers and other defects in nanodiamonds. The GF technique is computationally efficient and can simulate
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of integration, encompassing AM equipment, data, and simulation tools. To facilitate this, this opportunity focuses on building the necessary data and computation infrastructure to enable the Integration
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structure-property relationships for polymers has been largely limited due to the inability to systematically control polymer sequence especially under real-world conditions where process history
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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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industry, NIST recently established the NISTCHO Reference Material (RM 8675), a living cell line intended to benchmark biomanufacturing processes. However, CHO cell lines are known for their genomic
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NIST only participates in the February and August reviews. This research opportunity focuses on developing and improving the state-of-the-art methods for analysis, modeling, and simulation