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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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research on 2D materials, including ferroelectric, multiferroic, topological, and moiré systems. Develop, implement, and apply advanced simulation techniques (e.g., density functional theory, tight-binding
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energy density matter , High Energy Experimental , High Energy Physics , high energy physics or mathematical physics , High Energy Theory , High Energy Theory Group , High Performance Computing
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the group’s research and philosophy head over to www.d2r2group.com Qualifications Strong background in ab-initio calculations of materials (density functional theory) and high-performance computing
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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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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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on electronic structure and phonon dynamics in the part of the phase diagram of TMDs where short-range CDW fluctuations dominate. The methods that will be used include density functional theory and many-body
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, 7503–7507 (2015) • collaborate with theoreticians at CEMES to compare experiments with the band structure first-principles calculations based on the density functional theory (DFT). The National Intense
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strongly recommended. • High Performance Computing. • Experience with High Throughput Calculations will be valued but it is not essential. • Previous knowledge of Density Functional Theory (DFT) and
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including strongly correlated fermion materials, high-temperature superconductivity, topological electronic states of matter, developments and applications of computational methods at the density-functional