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many different methodological assumptions, that are currently based around simple single conformations of globular proteins. To address this gap, molecular dynamics (MD) simulations and Neutron
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causes of data variability to improve product quality and reproducibility [1]. Simulation Modeling: Developing theoretical and mathematical descriptions of physical phenomena, including both physics-based
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, materials modeling (including finite element simulations, and theory), and the development of a high-speed circuit to quantify fiber alignment in composites in real time. To develop this technique, a
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RAP opportunity at National Institute of Standards and Technology NIST Model Polyelectrolyte Solutions and Complexes Location Material Measurement Laboratory, Materials Science and Engineering
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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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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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materials, (2) the preferred binding sites of adsorbate species in nanoporous solids and predicted experimental signals (e.g., infrared spectra), and (3) the development of DFT-based force field models
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performance modeling capabilities that simultaneously consider multiple performance aspects, robust IAQ and other performance metrics, and measurement methods, sensors, and data to evaluate and verify building
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of products that incorporate billions of nanoscale features. Recent publications report our scatterfield microscopy techniques in which extensive electromagnetic modeling, instrument characterization, and data
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collaboration with the Ahn lab, we carried out molecular dynamics (MD) simulations of inactive and active states of ERK2, each extended out to 360 µs of total sampling [3,4]. The findings revealed differential