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to density functional theory (DFT) calculations and the generation of high-quality atomistic datasets for the development of analytical bond-order potentials (ADP) and machine-learned interatomic potentials
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-mechanical processing. Some of these modeling tools include density functional theory (DFT), CALPHAD-based models, phase-field models, and finite-element models (FEM) to predict as-built microsegregation
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, Physics, Materials Science, or a related field Three or more years of postdoctoral experience Strong background in first-principles density functional theory calculations, particularly simulations of light
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of spintronics. Complemented with density functional theory (DFT) calculations to build scientific and technical competence as well as strengthen transferrable skills, this position provides you with the skills
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] - Dynamical Mean-Field Theory (DMFT) and Density Functional Theory (DFT) - Density Matrix Embedding Theory (DMET) - Density Matrix Renormalization Group (DMRG) - Quantum Monte Carlo (QMC) - Tensor network
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and post-DFT methods) for generating datasets to train AI models leveraging DOE’s HPC platforms Develop new methodologies that can describe both atomic and spin relaxation accurately but at a much
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that operate at room temperature. In this project, Density Functional Theory (DFT) calculations will be used to simulate defect structures of h-BN, like substitutional, vacancy and interstitial defects
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simulation techniques, including density functional theory (DFT), molecular dynamics, Monte Carlo methods, and free‑energy perturbation calculations. Develop and implement novel computational methodologies and