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-driven materials design" https://www.nature.com/articles/s41524-020-00440-1 2. https://jarvis.nist.gov/ 3. https://www.nist.gov/people/kamal-choudhary Machine learning; Density functional theory; force
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of materials. The position requires extensive knowledge in performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to elucidate complex reaction mechanisms occurring
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to reduce the amount of required training data while maintaining high predictive accuracy. Methods and Techniques : Density Functional Theory, Machine Learning for atomistic modeling Location : Institut Jean
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Functional Theory (DFT) Familiarity with artificial intelligence methods Good knowledge of electronic structure methods Experience with Linux, Git and related tools Knowledge in the field of high-performance
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NIST only participates in the February and August reviews. First principles calculations, usually based on density functional theory (DFT), are a crucial aspect of modern materials physics research
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in structure characterization of homogeneous and heterogeneous catalysts using nuclear magnetic resonance (NMR) spectroscopy and density functional theory (DFT) to deduce structure-property
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. Advisers name email phone Carelyn E. Campbell carelyn.campbell@nist.gov 301.975.4920 Description First principles electronic structure methods such as density functional theory (DFT) are crucial
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atomistic simulation methods, such as molecular dynamics, density functional theory, and machine-learning force fields, to elucidate the deformation mechanisms activated by external stimuli. The candidate